{
  "name": "LLM VRAM dataset",
  "version": "2026-09-25",
  "first_published": "2026-09-25",
  "updated": "2026-09-25",
  "description": "Estimated memory for 29 open-weight language models at six quantisations and each common context length, and how each fits 20 GPUs. Architecture values are read from each model's config.json; GPU specs are the manufacturers'.",
  "publisher": "Nodegrove",
  "homepage": "https://nodegrove.io/data",
  "repository": "https://github.com/nodegrove/llm-vram-dataset",
  "license": "CC-BY-4.0",
  "license_url": "https://creativecommons.org/licenses/by/4.0/",
  "doi": "10.5281/zenodo.22966137",
  "citation": "Nodegrove (2026). LLM VRAM dataset (version 2026-09-25) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.22966137",
  "findings": [
    {
      "headline": "Newer models pay far less for context.",
      "detail": "At 32k tokens, Qwen3 32B (2025, standard attention) spends 8.6 GB on its KV cache; its successor Qwen3.8 27B (2026, hybrid attention) spends 2.3 GB. Across all 29 models, each 1,000 tokens of context costs between 0.006 GB (Nemotron 3.5 Lightning 30B-A3B) and 0.328 GB (DeepSeek-R1 Distill Llama 70B)."
    },
    {
      "headline": "Active parameters set the speed, not the memory.",
      "detail": "Qwen3-Coder-Next 80B-A3B reads 3B parameters per token but keeps all 79.7B in memory: 48.9 GB at Q4_K_M with 8k context, against 22.4 GB for the dense Qwen3 32B."
    },
    {
      "headline": "24 of 29 models fit a 24 GB card at Q4_K_M with 8k context.",
      "detail": "The largest is Qwen3.6 35B-A3B at 22.4 GB, under the 22.8 GB line (95% of the card)."
    }
  ],
  "files": [
    {
      "name": "llm-vram-models.csv",
      "url": "https://nodegrove.io/data/llm-vram-models.csv",
      "rows": 29,
      "about": "One row per model: size, attention design, context window, licence and the source of every value"
    },
    {
      "name": "llm-vram-gpus.csv",
      "url": "https://nodegrove.io/data/llm-vram-gpus.csv",
      "rows": 20,
      "about": "One row per GPU: memory, the memory a runtime can use, bandwidth and the specification page"
    },
    {
      "name": "llm-vram-requirements.csv",
      "url": "https://nodegrove.io/data/llm-vram-requirements.csv",
      "rows": 1068,
      "about": "Memory per model, quantisation and context length, split into weights, KV cache and overhead"
    },
    {
      "name": "llm-vram-gpu-fit.csv",
      "url": "https://nodegrove.io/data/llm-vram-gpu-fit.csv",
      "rows": 3480,
      "about": "Every model on every GPU at every quantisation, at 8k context: verdict, longest context that fits and speed ceiling"
    },
    {
      "name": "llm-vram.json",
      "url": "https://nodegrove.io/data/llm-vram.json",
      "rows": null,
      "about": "Everything above in one file, with the method, its constants and each model's cache layout"
    }
  ],
  "method": {
    "total_gb": "weights_gb + kv_cache_gb + overhead_gb",
    "weights_gb": "params_b × bytes_per_param",
    "kv_cache_gb": "For each group of attention layers: layers × values cached per token × min(context, window) × 2 bytes (FP16) or 1 byte (Q8), plus fixed_state_gb. See kv_groups on each model",
    "overhead_gb": "0.5 + 0.04 × weights_gb",
    "fit": "A model fits when need_gb ≤ 0.95 × usable_gb; tight when need_gb > 0.85 × usable_gb",
    "tokens_per_second_ceiling": "0.7 × bandwidth_gb_s ÷ (active parameters, billions × bytes_per_param). Single stream, decode only; a ceiling, not a measurement",
    "constants": {
      "overhead_fixed_gb": 0.5,
      "overhead_share_of_weights": 0.04,
      "headroom": 0.95,
      "tight_above": 0.85,
      "apple_gpu_share": 0.75,
      "speed_efficiency": 0.7,
      "fit_context_tokens": 8192,
      "max_context_tokens": 131072
    }
  },
  "quants": [
    {
      "quant": "FP16",
      "bytes_per_param": 2,
      "note": "full precision, best quality, largest"
    },
    {
      "quant": "Q8_0",
      "bytes_per_param": 1.06,
      "note": "near-lossless"
    },
    {
      "quant": "Q6_K",
      "bytes_per_param": 0.82,
      "note": "very close to Q8"
    },
    {
      "quant": "Q5_K_M",
      "bytes_per_param": 0.71,
      "note": "good balance"
    },
    {
      "quant": "Q4_K_M",
      "bytes_per_param": 0.58,
      "note": "the common default; small quality loss"
    },
    {
      "quant": "Q3_K_M",
      "bytes_per_param": 0.47,
      "note": "noticeable loss; last resort"
    }
  ],
  "columns": {
    "models": {
      "model_id": "Stable identifier, also the page slug on nodegrove.io",
      "model": "Model name",
      "family": "Model family",
      "released": "Month the weights were published (YYYY-MM)",
      "params_b": "Total parameters, billions, as the checkpoint reports them. Mixture-of-experts models count every expert; multimodal checkpoints include the vision encoder",
      "active_params_b": "Parameters read per token, billions. Mixture-of-experts models only; blank for dense models",
      "attention": "How the model caches context: standard, sliding-window, hybrid or latent (see the method)",
      "layers": "Transformer layers (blocks)",
      "kv_heads": "Key/value heads of the main attention layers",
      "head_dim": "Dimension of each key/value head",
      "full_cache_layers": "Layers whose cache grows with the whole context",
      "window_layers": "Layers that only keep the last window_tokens tokens",
      "window_tokens": "Sliding-window length in tokens; blank if the model has none",
      "kv_cache_gb_per_1k_tokens": "Memory each extra 1,000 tokens of context costs, GB, with an FP16 cache once any windows are full",
      "fixed_state_gb": "Fixed recurrent state of linear-attention or Mamba layers, GB. Does not grow with context",
      "context_tokens": "Native context window in tokens (max_position_embeddings, or the model card where the two differ)",
      "license": "Licence of the weights, from the model card",
      "license_permissive": "true when the licence has no field-of-use or scale conditions",
      "role": "code or reasoning for specialist models; blank for general-purpose models",
      "successor_id": "Newer model in the same family and role, if there is one",
      "hf_repo": "Hugging Face repository the values were read from",
      "hf_gated": "true when config.json opens only after accepting the licence on Hugging Face",
      "config_url": "Link to the config.json the architecture values come from",
      "page_url": "The model page on nodegrove.io",
      "attention_detail": "The attention layout in one sentence",
      "notes": "Caveats that change how the numbers should be read"
    },
    "gpus": {
      "gpu_id": "Stable identifier, also the page slug on nodegrove.io",
      "gpu": "Card or machine",
      "kind": "consumer, workstation, datacenter, apple, or unified (non-Apple unified memory)",
      "maker": "NVIDIA, AMD, Apple or Intel",
      "memory_gb": "Memory from the manufacturer specification, GB (unified memory for Apple silicon)",
      "usable_gb": "Memory an inference runtime can address, GB. Apple silicon: about 75% of unified memory (observed; Apple publishes no figure). Other unified-memory machines: the share their maker documents, stated on the GPU page",
      "budget_gb": "95% of usable_gb: the line a model must fit under to count as fitting",
      "bandwidth_gb_s": "Memory bandwidth from the manufacturer specification, GB/s",
      "spec_url": "The manufacturer's specification page",
      "bandwidth_source_url": "Where the bandwidth figure is printed, when spec_url does not print it (a maker whitepaper, datasheet or announcement, or the data-rate source for a stated bus × rate calculation). Empty when spec_url prints it",
      "page_url": "The GPU page on nodegrove.io",
      "notes": "Caveats"
    },
    "requirements": {
      "model_id": "Joins llm-vram-models.csv",
      "model": "Model name",
      "quant": "FP16, Q8_0, Q6_K, Q5_K_M, Q4_K_M or Q3_K_M",
      "bytes_per_param": "Effective bytes per parameter at that quantisation",
      "context_tokens": "Tokens held in context",
      "weights_gb": "Parameters × bytes_per_param, GB",
      "kv_cache_gb": "KV cache stored at FP16, plus any fixed state, GB",
      "overhead_gb": "Runtime context and buffers: 0.5 GB + 4% of weights",
      "total_gb": "weights_gb + kv_cache_gb + overhead_gb",
      "total_gb_q8_cache": "The same total with the KV cache quantised to 8 bits"
    },
    "fit": {
      "model_id": "Joins llm-vram-models.csv",
      "model": "Model name",
      "gpu_id": "Joins llm-vram-gpus.csv",
      "gpu": "Card or machine",
      "quant": "Quantisation of the weights",
      "context_tokens": "Context the fit is computed at (8,192 tokens)",
      "need_gb": "Estimated memory at that quantisation and context, GB (total_gb in llm-vram-requirements.csv)",
      "verdict": "fits: need_gb is within the GPU's budget_gb. tight: it fits, but above 85% of usable memory. no: it does not fit",
      "max_context_tokens": "Longest context that fits at this quantisation with an FP16 cache, capped at 131,072 tokens or the model's window. 0 when the weights alone do not fit",
      "tokens_per_second_ceiling": "Upper bound on single-stream generation speed, tokens/s (see the method). Blank when the model cannot load"
    }
  },
  "models": [
    {"model_id":"llama-3.2-3b","model":"Llama 3.2 3B","family":"Llama","released":"2024-09","params_b":3.2,"active_params_b":null,"attention":"standard","layers":28,"kv_heads":8,"head_dim":128,"full_cache_layers":28,"window_layers":0,"window_tokens":null,"kv_cache_gb_per_1k_tokens":0.115,"fixed_state_gb":null,"context_tokens":131072,"license":"Llama Community License","license_permissive":false,"role":null,"successor_id":null,"hf_repo":"meta-llama/Llama-3.2-3B","hf_gated":true,"config_url":"https://huggingface.co/meta-llama/Llama-3.2-3B/blob/main/config.json","page_url":"https://nodegrove.io/models/llama-3.2-3b","attention_detail":"28 layers, all full attention, with 8 KV heads of dimension 128","notes":null,"kv_groups":[{"layers":28,"values_per_token":2048,"window_tokens":null}]},
    {"model_id":"qwen3.5-4b","model":"Qwen3.5 4B","family":"Qwen","released":"2026-02","params_b":4.7,"active_params_b":null,"attention":"hybrid","layers":32,"kv_heads":4,"head_dim":256,"full_cache_layers":8,"window_layers":0,"window_tokens":null,"kv_cache_gb_per_1k_tokens":0.033,"fixed_state_gb":0.05,"context_tokens":262144,"license":"Apache 2.0","license_permissive":true,"role":null,"successor_id":null,"hf_repo":"Qwen/Qwen3.5-4B","hf_gated":false,"config_url":"https://huggingface.co/Qwen/Qwen3.5-4B/blob/main/config.json","page_url":"https://nodegrove.io/models/qwen3.5-4b","attention_detail":"32 layers, of which only 8 are full attention (4 KV heads of dimension 256) and keep a cache that grows with the conversation; the other 24 are Gated DeltaNet linear-attention layers with a small fixed state","notes":"The parameter count includes the vision encoder; a text-only GGUF is slightly smaller.","kv_groups":[{"layers":8,"values_per_token":2048,"window_tokens":null}]},
    {"model_id":"llama-3.1-8b","model":"Llama 3.1 8B","family":"Llama","released":"2024-07","params_b":8,"active_params_b":null,"attention":"standard","layers":32,"kv_heads":8,"head_dim":128,"full_cache_layers":32,"window_layers":0,"window_tokens":null,"kv_cache_gb_per_1k_tokens":0.131,"fixed_state_gb":null,"context_tokens":131072,"license":"Llama Community License","license_permissive":false,"role":null,"successor_id":null,"hf_repo":"meta-llama/Llama-3.1-8B","hf_gated":true,"config_url":"https://huggingface.co/meta-llama/Llama-3.1-8B/blob/main/config.json","page_url":"https://nodegrove.io/models/llama-3.1-8b","attention_detail":"32 layers, all full attention, with 8 KV heads of dimension 128","notes":null,"kv_groups":[{"layers":32,"values_per_token":2048,"window_tokens":null}]},
    {"model_id":"qwen3-8b","model":"Qwen3 8B","family":"Qwen","released":"2025-04","params_b":8.2,"active_params_b":null,"attention":"standard","layers":36,"kv_heads":8,"head_dim":128,"full_cache_layers":36,"window_layers":0,"window_tokens":null,"kv_cache_gb_per_1k_tokens":0.147,"fixed_state_gb":null,"context_tokens":40960,"license":"Apache 2.0","license_permissive":true,"role":null,"successor_id":"qwen3.5-9b","hf_repo":"Qwen/Qwen3-8B","hf_gated":false,"config_url":"https://huggingface.co/Qwen/Qwen3-8B/blob/main/config.json","page_url":"https://nodegrove.io/models/qwen3-8b","attention_detail":"36 layers, all full attention, with 8 KV heads of dimension 128","notes":null,"kv_groups":[{"layers":36,"values_per_token":2048,"window_tokens":null}]},
    {"model_id":"qwen3.5-9b","model":"Qwen3.5 9B","family":"Qwen","released":"2026-02","params_b":9.7,"active_params_b":null,"attention":"hybrid","layers":32,"kv_heads":4,"head_dim":256,"full_cache_layers":8,"window_layers":0,"window_tokens":null,"kv_cache_gb_per_1k_tokens":0.033,"fixed_state_gb":0.05,"context_tokens":262144,"license":"Apache 2.0","license_permissive":true,"role":null,"successor_id":null,"hf_repo":"Qwen/Qwen3.5-9B","hf_gated":false,"config_url":"https://huggingface.co/Qwen/Qwen3.5-9B/blob/main/config.json","page_url":"https://nodegrove.io/models/qwen3.5-9b","attention_detail":"32 layers, of which only 8 are full attention (4 KV heads of dimension 256) and keep a cache that grows with the conversation; the other 24 are Gated DeltaNet linear-attention layers with a small fixed state","notes":"The parameter count includes the vision encoder; a text-only GGUF is slightly smaller.","kv_groups":[{"layers":8,"values_per_token":2048,"window_tokens":null}]},
    {"model_id":"gemma-4-12b","model":"Gemma 4 12B","family":"Gemma","released":"2026-05","params_b":12,"active_params_b":null,"attention":"sliding-window","layers":48,"kv_heads":8,"head_dim":256,"full_cache_layers":8,"window_layers":40,"window_tokens":1024,"kv_cache_gb_per_1k_tokens":0.016,"fixed_state_gb":null,"context_tokens":262144,"license":"Apache 2.0","license_permissive":true,"role":null,"successor_id":null,"hf_repo":"google/gemma-4-12B-it","hf_gated":false,"config_url":"https://huggingface.co/google/gemma-4-12B-it/blob/main/config.json","page_url":"https://nodegrove.io/models/gemma-4-12b","attention_detail":"48 layers: 40 use sliding-window attention and only remember the last 1,024 tokens (8 KV heads of dimension 256), and 8 are global layers with a single KV head of dimension 512","notes":"A runtime without a sliding-window cache stores every layer at full length and needs far more than this at long context.","kv_groups":[{"layers":8,"values_per_token":1024,"window_tokens":null},{"layers":40,"values_per_token":4096,"window_tokens":1024}]},
    {"model_id":"gemma-3-12b","model":"Gemma 3 12B","family":"Gemma","released":"2025-03","params_b":12.2,"active_params_b":null,"attention":"sliding-window","layers":48,"kv_heads":8,"head_dim":256,"full_cache_layers":8,"window_layers":40,"window_tokens":1024,"kv_cache_gb_per_1k_tokens":0.066,"fixed_state_gb":null,"context_tokens":131072,"license":"Gemma Terms of Use","license_permissive":false,"role":null,"successor_id":"gemma-4-12b","hf_repo":"google/gemma-3-12b-it","hf_gated":true,"config_url":"https://huggingface.co/google/gemma-3-12b-it/blob/main/config.json","page_url":"https://nodegrove.io/models/gemma-3-12b","attention_detail":"48 layers with 8 KV heads of dimension 256; five in every six use sliding-window attention and only remember the last 1,024 tokens, so 8 global layers carry the long context","notes":"A runtime without a sliding-window cache stores every layer at full length and needs far more than this at long context.","kv_groups":[{"layers":8,"values_per_token":4096,"window_tokens":null},{"layers":40,"values_per_token":4096,"window_tokens":1024}]},
    {"model_id":"ministral-3-14b","model":"Ministral 3 14B","family":"Mistral","released":"2025-12","params_b":14,"active_params_b":null,"attention":"standard","layers":40,"kv_heads":8,"head_dim":128,"full_cache_layers":40,"window_layers":0,"window_tokens":null,"kv_cache_gb_per_1k_tokens":0.164,"fixed_state_gb":null,"context_tokens":262144,"license":"Apache 2.0","license_permissive":true,"role":null,"successor_id":null,"hf_repo":"mistralai/Ministral-3-14B-Instruct-2512","hf_gated":false,"config_url":"https://huggingface.co/mistralai/Ministral-3-14B-Instruct-2512/blob/main/config.json","page_url":"https://nodegrove.io/models/ministral-3-14b","attention_detail":"40 layers, all full attention, with 8 KV heads of dimension 128","notes":"The parameter count includes the vision encoder; a text-only GGUF is slightly smaller.","kv_groups":[{"layers":40,"values_per_token":2048,"window_tokens":null}]},
    {"model_id":"qwen3-14b","model":"Qwen3 14B","family":"Qwen","released":"2025-04","params_b":14.8,"active_params_b":null,"attention":"standard","layers":40,"kv_heads":8,"head_dim":128,"full_cache_layers":40,"window_layers":0,"window_tokens":null,"kv_cache_gb_per_1k_tokens":0.164,"fixed_state_gb":null,"context_tokens":40960,"license":"Apache 2.0","license_permissive":true,"role":null,"successor_id":null,"hf_repo":"Qwen/Qwen3-14B","hf_gated":false,"config_url":"https://huggingface.co/Qwen/Qwen3-14B/blob/main/config.json","page_url":"https://nodegrove.io/models/qwen3-14b","attention_detail":"40 layers, all full attention, with 8 KV heads of dimension 128","notes":null,"kv_groups":[{"layers":40,"values_per_token":2048,"window_tokens":null}]},
    {"model_id":"phi-4","model":"Phi-4 14B","family":"Phi","released":"2024-12","params_b":14.7,"active_params_b":null,"attention":"standard","layers":40,"kv_heads":10,"head_dim":128,"full_cache_layers":40,"window_layers":0,"window_tokens":null,"kv_cache_gb_per_1k_tokens":0.205,"fixed_state_gb":null,"context_tokens":16384,"license":"MIT","license_permissive":true,"role":null,"successor_id":null,"hf_repo":"microsoft/phi-4","hf_gated":false,"config_url":"https://huggingface.co/microsoft/phi-4/blob/main/config.json","page_url":"https://nodegrove.io/models/phi-4","attention_detail":"40 layers, all full attention, with 10 KV heads of dimension 128","notes":null,"kv_groups":[{"layers":40,"values_per_token":2560,"window_tokens":null}]},
    {"model_id":"gpt-oss-20b","model":"gpt-oss 20B","family":"gpt-oss","released":"2025-08","params_b":21,"active_params_b":3.6,"attention":"sliding-window","layers":24,"kv_heads":8,"head_dim":64,"full_cache_layers":12,"window_layers":12,"window_tokens":128,"kv_cache_gb_per_1k_tokens":0.025,"fixed_state_gb":null,"context_tokens":131072,"license":"Apache 2.0","license_permissive":true,"role":null,"successor_id":null,"hf_repo":"openai/gpt-oss-20b","hf_gated":false,"config_url":"https://huggingface.co/openai/gpt-oss-20b/blob/main/config.json","page_url":"https://nodegrove.io/models/gpt-oss-20b","attention_detail":"24 layers with 8 KV heads of dimension 64, alternating full attention with a 128-token sliding window, so only half the layers keep a cache that grows","notes":"Ships in MXFP4; roughly 4.25 bits per expert weight. Choose the 4-bit row.","kv_groups":[{"layers":12,"values_per_token":1024,"window_tokens":null},{"layers":12,"values_per_token":1024,"window_tokens":128}]},
    {"model_id":"devstral-small-2-24b","model":"Devstral Small 2 24B","family":"Mistral","released":"2025-12","params_b":24,"active_params_b":null,"attention":"standard","layers":40,"kv_heads":8,"head_dim":128,"full_cache_layers":40,"window_layers":0,"window_tokens":null,"kv_cache_gb_per_1k_tokens":0.164,"fixed_state_gb":null,"context_tokens":262144,"license":"Apache 2.0","license_permissive":true,"role":"code","successor_id":null,"hf_repo":"mistralai/Devstral-Small-2-24B-Instruct-2512","hf_gated":false,"config_url":"https://huggingface.co/mistralai/Devstral-Small-2-24B-Instruct-2512/blob/main/config.json","page_url":"https://nodegrove.io/models/devstral-small-2-24b","attention_detail":"40 layers, all full attention, with 8 KV heads of dimension 128","notes":"Mistral's coding model. The parameter count includes the vision encoder; a text-only GGUF is slightly smaller.","kv_groups":[{"layers":40,"values_per_token":2048,"window_tokens":null}]},
    {"model_id":"mistral-small-3.1-24b","model":"Mistral Small 3.1 24B","family":"Mistral","released":"2025-03","params_b":24,"active_params_b":null,"attention":"standard","layers":40,"kv_heads":8,"head_dim":128,"full_cache_layers":40,"window_layers":0,"window_tokens":null,"kv_cache_gb_per_1k_tokens":0.164,"fixed_state_gb":null,"context_tokens":131072,"license":"Apache 2.0","license_permissive":true,"role":null,"successor_id":"mistral-small-4-119b","hf_repo":"mistralai/Mistral-Small-3.1-24B-Instruct-2503","hf_gated":false,"config_url":"https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/blob/main/config.json","page_url":"https://nodegrove.io/models/mistral-small-3.1-24b","attention_detail":"40 layers, all full attention, with 8 KV heads of dimension 128","notes":null,"kv_groups":[{"layers":40,"values_per_token":2048,"window_tokens":null}]},
    {"model_id":"gemma-4-26b-a4b","model":"Gemma 4 26B-A4B (MoE)","family":"Gemma","released":"2026-03","params_b":25.8,"active_params_b":3.8,"attention":"sliding-window","layers":30,"kv_heads":8,"head_dim":256,"full_cache_layers":5,"window_layers":25,"window_tokens":1024,"kv_cache_gb_per_1k_tokens":0.02,"fixed_state_gb":null,"context_tokens":262144,"license":"Apache 2.0","license_permissive":true,"role":null,"successor_id":null,"hf_repo":"google/gemma-4-26B-A4B-it","hf_gated":false,"config_url":"https://huggingface.co/google/gemma-4-26B-A4B-it/blob/main/config.json","page_url":"https://nodegrove.io/models/gemma-4-26b-a4b","attention_detail":"30 layers: 25 use sliding-window attention over the last 1,024 tokens (8 KV heads of dimension 256), and 5 are global layers with 2 KV heads of dimension 512","notes":"All 128 experts stay in memory; 3.8B are active per token. The parameter count includes the vision encoder; a text-only GGUF is slightly smaller.","kv_groups":[{"layers":5,"values_per_token":2048,"window_tokens":null},{"layers":25,"values_per_token":4096,"window_tokens":1024}]},
    {"model_id":"gemma-3-27b","model":"Gemma 3 27B","family":"Gemma","released":"2025-03","params_b":27.4,"active_params_b":null,"attention":"sliding-window","layers":62,"kv_heads":16,"head_dim":128,"full_cache_layers":10,"window_layers":52,"window_tokens":1024,"kv_cache_gb_per_1k_tokens":0.082,"fixed_state_gb":null,"context_tokens":131072,"license":"Gemma Terms of Use","license_permissive":false,"role":null,"successor_id":"gemma-4-31b","hf_repo":"google/gemma-3-27b-it","hf_gated":true,"config_url":"https://huggingface.co/google/gemma-3-27b-it/blob/main/config.json","page_url":"https://nodegrove.io/models/gemma-3-27b","attention_detail":"62 layers with 16 KV heads of dimension 128; five in every six use sliding-window attention and only remember the last 1,024 tokens, so 10 global layers carry the long context","notes":"A runtime without a sliding-window cache stores every layer at full length and needs far more than this at long context.","kv_groups":[{"layers":10,"values_per_token":4096,"window_tokens":null},{"layers":52,"values_per_token":4096,"window_tokens":1024}]},
    {"model_id":"qwen3.8-27b","model":"Qwen3.8 27B","family":"Qwen","released":"2026-08","params_b":27.8,"active_params_b":null,"attention":"hybrid","layers":64,"kv_heads":4,"head_dim":256,"full_cache_layers":16,"window_layers":0,"window_tokens":null,"kv_cache_gb_per_1k_tokens":0.066,"fixed_state_gb":0.15,"context_tokens":262144,"license":"Apache 2.0","license_permissive":true,"role":null,"successor_id":null,"hf_repo":"Qwen/Qwen3.8-27B","hf_gated":false,"config_url":"https://huggingface.co/Qwen/Qwen3.8-27B/blob/main/config.json","page_url":"https://nodegrove.io/models/qwen3.8-27b","attention_detail":"64 layers, of which only 16 are full attention (4 KV heads of dimension 256) and keep a cache that grows with the conversation; the other 48 are Gated DeltaNet linear-attention layers with a fixed state of about 0.15 GB","notes":"The parameter count includes the vision encoder; a text-only GGUF is slightly smaller.","kv_groups":[{"layers":16,"values_per_token":2048,"window_tokens":null}]},
    {"model_id":"qwen3-30b-a3b","model":"Qwen3 30B-A3B (MoE)","family":"Qwen","released":"2025-04","params_b":30.5,"active_params_b":3.3,"attention":"standard","layers":48,"kv_heads":4,"head_dim":128,"full_cache_layers":48,"window_layers":0,"window_tokens":null,"kv_cache_gb_per_1k_tokens":0.098,"fixed_state_gb":null,"context_tokens":40960,"license":"Apache 2.0","license_permissive":true,"role":null,"successor_id":"qwen3.6-35b-a3b","hf_repo":"Qwen/Qwen3-30B-A3B","hf_gated":false,"config_url":"https://huggingface.co/Qwen/Qwen3-30B-A3B/blob/main/config.json","page_url":"https://nodegrove.io/models/qwen3-30b-a3b","attention_detail":"48 layers, all full attention, with 4 KV heads of dimension 128","notes":"All experts stay in memory; only 3.3B are active per token, so it is fast for its size.","kv_groups":[{"layers":48,"values_per_token":1024,"window_tokens":null}]},
    {"model_id":"glm-4.7-flash","model":"GLM-4.7 Flash 30B-A3B (MoE)","family":"GLM","released":"2026-01","params_b":31.2,"active_params_b":3,"attention":"latent","layers":47,"kv_heads":20,"head_dim":256,"full_cache_layers":47,"window_layers":0,"window_tokens":null,"kv_cache_gb_per_1k_tokens":0.054,"fixed_state_gb":null,"context_tokens":202752,"license":"MIT","license_permissive":true,"role":null,"successor_id":null,"hf_repo":"zai-org/GLM-4.7-Flash","hf_gated":false,"config_url":"https://huggingface.co/zai-org/GLM-4.7-Flash/blob/main/config.json","page_url":"https://nodegrove.io/models/glm-4.7-flash","attention_detail":"47 layers using multi-head latent attention: instead of keys and values for each of its 20 heads, every layer caches one compressed vector of 576 values per token","notes":"A runtime that does not implement the latent cache stores full keys and values and needs several times more context memory.","kv_groups":[{"layers":47,"values_per_token":576,"window_tokens":null}]},
    {"model_id":"gemma-4-31b","model":"Gemma 4 31B","family":"Gemma","released":"2026-03","params_b":31.3,"active_params_b":null,"attention":"sliding-window","layers":60,"kv_heads":16,"head_dim":256,"full_cache_layers":10,"window_layers":50,"window_tokens":1024,"kv_cache_gb_per_1k_tokens":0.082,"fixed_state_gb":null,"context_tokens":262144,"license":"Apache 2.0","license_permissive":true,"role":null,"successor_id":null,"hf_repo":"google/gemma-4-31B-it","hf_gated":false,"config_url":"https://huggingface.co/google/gemma-4-31B-it/blob/main/config.json","page_url":"https://nodegrove.io/models/gemma-4-31b","attention_detail":"60 layers: 50 use sliding-window attention over the last 1,024 tokens (16 KV heads of dimension 256), and 10 are global layers with 4 KV heads of dimension 512","notes":"A runtime without a sliding-window cache stores every layer at full length and needs far more than this at long context. The parameter count includes the vision encoder; a text-only GGUF is slightly smaller.","kv_groups":[{"layers":10,"values_per_token":4096,"window_tokens":null},{"layers":50,"values_per_token":8192,"window_tokens":1024}]},
    {"model_id":"nemotron-3.5-lightning-30b-a3b","model":"Nemotron 3.5 Lightning 30B-A3B (MoE)","family":"Nemotron","released":"2026-08","params_b":31.6,"active_params_b":3,"attention":"hybrid","layers":52,"kv_heads":2,"head_dim":128,"full_cache_layers":6,"window_layers":0,"window_tokens":null,"kv_cache_gb_per_1k_tokens":0.006,"fixed_state_gb":0.05,"context_tokens":262144,"license":"OpenMDW 1.1","license_permissive":false,"role":null,"successor_id":null,"hf_repo":"nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16","hf_gated":false,"config_url":"https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16/blob/main/config.json","page_url":"https://nodegrove.io/models/nemotron-3.5-lightning-30b-a3b","attention_detail":"52 blocks, of which only 6 are attention layers (2 KV heads of dimension 128); 23 are Mamba-2 layers with a small fixed state and 23 are mixture-of-experts layers with no cache at all","notes":null,"kv_groups":[{"layers":6,"values_per_token":512,"window_tokens":null}]},
    {"model_id":"qwen3-32b","model":"Qwen3 32B","family":"Qwen","released":"2025-04","params_b":32.8,"active_params_b":null,"attention":"standard","layers":64,"kv_heads":8,"head_dim":128,"full_cache_layers":64,"window_layers":0,"window_tokens":null,"kv_cache_gb_per_1k_tokens":0.262,"fixed_state_gb":null,"context_tokens":40960,"license":"Apache 2.0","license_permissive":true,"role":null,"successor_id":"qwen3.8-27b","hf_repo":"Qwen/Qwen3-32B","hf_gated":false,"config_url":"https://huggingface.co/Qwen/Qwen3-32B/blob/main/config.json","page_url":"https://nodegrove.io/models/qwen3-32b","attention_detail":"64 layers, all full attention, with 8 KV heads of dimension 128","notes":null,"kv_groups":[{"layers":64,"values_per_token":2048,"window_tokens":null}]},
    {"model_id":"qwen2.5-coder-32b","model":"Qwen2.5 Coder 32B","family":"Qwen","released":"2024-11","params_b":32.8,"active_params_b":null,"attention":"standard","layers":64,"kv_heads":8,"head_dim":128,"full_cache_layers":64,"window_layers":0,"window_tokens":null,"kv_cache_gb_per_1k_tokens":0.262,"fixed_state_gb":null,"context_tokens":32768,"license":"Apache 2.0","license_permissive":true,"role":"code","successor_id":"qwen3-coder-next","hf_repo":"Qwen/Qwen2.5-Coder-32B-Instruct","hf_gated":false,"config_url":"https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct/blob/main/config.json","page_url":"https://nodegrove.io/models/qwen2.5-coder-32b","attention_detail":"64 layers, all full attention, with 8 KV heads of dimension 128","notes":null,"kv_groups":[{"layers":64,"values_per_token":2048,"window_tokens":null}]},
    {"model_id":"deepseek-r1-distill-qwen-32b","model":"DeepSeek-R1 Distill Qwen 32B","family":"DeepSeek","released":"2025-01","params_b":32.8,"active_params_b":null,"attention":"standard","layers":64,"kv_heads":8,"head_dim":128,"full_cache_layers":64,"window_layers":0,"window_tokens":null,"kv_cache_gb_per_1k_tokens":0.262,"fixed_state_gb":null,"context_tokens":131072,"license":"MIT","license_permissive":true,"role":"reasoning","successor_id":null,"hf_repo":"deepseek-ai/DeepSeek-R1-Distill-Qwen-32B","hf_gated":false,"config_url":"https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B/blob/main/config.json","page_url":"https://nodegrove.io/models/deepseek-r1-distill-qwen-32b","attention_detail":"64 layers, all full attention, with 8 KV heads of dimension 128","notes":null,"kv_groups":[{"layers":64,"values_per_token":2048,"window_tokens":null}]},
    {"model_id":"qwen3.6-35b-a3b","model":"Qwen3.6 35B-A3B (MoE)","family":"Qwen","released":"2026-04","params_b":36,"active_params_b":3,"attention":"hybrid","layers":40,"kv_heads":2,"head_dim":256,"full_cache_layers":10,"window_layers":0,"window_tokens":null,"kv_cache_gb_per_1k_tokens":0.02,"fixed_state_gb":0.06,"context_tokens":262144,"license":"Apache 2.0","license_permissive":true,"role":null,"successor_id":null,"hf_repo":"Qwen/Qwen3.6-35B-A3B","hf_gated":false,"config_url":"https://huggingface.co/Qwen/Qwen3.6-35B-A3B/blob/main/config.json","page_url":"https://nodegrove.io/models/qwen3.6-35b-a3b","attention_detail":"40 layers, of which only 10 are full attention (2 KV heads of dimension 256) and keep a cache that grows with the conversation; the other 30 are Gated DeltaNet linear-attention layers with a small fixed state","notes":"All 256 experts stay in memory; 3B are active per token. The parameter count includes the vision encoder; a text-only GGUF is slightly smaller.","kv_groups":[{"layers":10,"values_per_token":1024,"window_tokens":null}]},
    {"model_id":"llama-3.3-70b","model":"Llama 3.3 70B","family":"Llama","released":"2024-12","params_b":70.6,"active_params_b":null,"attention":"standard","layers":80,"kv_heads":8,"head_dim":128,"full_cache_layers":80,"window_layers":0,"window_tokens":null,"kv_cache_gb_per_1k_tokens":0.328,"fixed_state_gb":null,"context_tokens":131072,"license":"Llama Community License","license_permissive":false,"role":null,"successor_id":null,"hf_repo":"meta-llama/Llama-3.3-70B-Instruct","hf_gated":true,"config_url":"https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct/blob/main/config.json","page_url":"https://nodegrove.io/models/llama-3.3-70b","attention_detail":"80 layers, all full attention, with 8 KV heads of dimension 128","notes":null,"kv_groups":[{"layers":80,"values_per_token":2048,"window_tokens":null}]},
    {"model_id":"deepseek-r1-distill-llama-70b","model":"DeepSeek-R1 Distill Llama 70B","family":"DeepSeek","released":"2025-01","params_b":70.6,"active_params_b":null,"attention":"standard","layers":80,"kv_heads":8,"head_dim":128,"full_cache_layers":80,"window_layers":0,"window_tokens":null,"kv_cache_gb_per_1k_tokens":0.328,"fixed_state_gb":null,"context_tokens":131072,"license":"MIT","license_permissive":true,"role":"reasoning","successor_id":null,"hf_repo":"deepseek-ai/DeepSeek-R1-Distill-Llama-70B","hf_gated":false,"config_url":"https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-70B/blob/main/config.json","page_url":"https://nodegrove.io/models/deepseek-r1-distill-llama-70b","attention_detail":"80 layers, all full attention, with 8 KV heads of dimension 128","notes":null,"kv_groups":[{"layers":80,"values_per_token":2048,"window_tokens":null}]},
    {"model_id":"qwen3-coder-next","model":"Qwen3-Coder-Next 80B-A3B (MoE)","family":"Qwen","released":"2026-01","params_b":79.7,"active_params_b":3,"attention":"hybrid","layers":48,"kv_heads":2,"head_dim":256,"full_cache_layers":12,"window_layers":0,"window_tokens":null,"kv_cache_gb_per_1k_tokens":0.025,"fixed_state_gb":0.08,"context_tokens":262144,"license":"Apache 2.0","license_permissive":true,"role":"code","successor_id":null,"hf_repo":"Qwen/Qwen3-Coder-Next","hf_gated":false,"config_url":"https://huggingface.co/Qwen/Qwen3-Coder-Next/blob/main/config.json","page_url":"https://nodegrove.io/models/qwen3-coder-next","attention_detail":"48 layers, of which only 12 are full attention (2 KV heads of dimension 256) and keep a cache that grows with the conversation; the other 36 are Gated DeltaNet linear-attention layers with a small fixed state","notes":"All 512 experts stay in memory; 3B are active per token, so it answers like a small model once it is loaded.","kv_groups":[{"layers":12,"values_per_token":1024,"window_tokens":null}]},
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    {"model_id":"mistral-small-4-119b","model":"Mistral Small 4 119B (MoE)","family":"Mistral","released":"2026-03","params_b":119,"active_params_b":6.5,"attention":"latent","layers":36,"kv_heads":32,"head_dim":128,"full_cache_layers":36,"window_layers":0,"window_tokens":null,"kv_cache_gb_per_1k_tokens":0.023,"fixed_state_gb":null,"context_tokens":262144,"license":"Apache 2.0","license_permissive":true,"role":null,"successor_id":null,"hf_repo":"mistralai/Mistral-Small-4-119B-2603","hf_gated":false,"config_url":"https://huggingface.co/mistralai/Mistral-Small-4-119B-2603/blob/main/config.json","page_url":"https://nodegrove.io/models/mistral-small-4-119b","attention_detail":"36 layers using multi-head latent attention: instead of keys and values for each of its 32 heads, every layer caches one compressed vector of 320 values per token","notes":"Small in name only: all 128 experts stay in memory. A runtime that does not implement the latent cache needs several times more context memory. The parameter count includes the vision encoder; a text-only GGUF is slightly smaller.","kv_groups":[{"layers":36,"values_per_token":320,"window_tokens":null}]}
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  "gpus": [
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    {"gpu_id":"rtx-4060-ti-16","gpu":"RTX 4060 Ti 16 GB","kind":"consumer","maker":"NVIDIA","memory_gb":16,"usable_gb":16,"budget_gb":15.2,"bandwidth_gb_s":288,"spec_url":"https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4060-4060ti/","bandwidth_source_url":"https://marketplace.nvidia.com/en-us/consumer/graphics-cards/msi-geforce-rtx-4060-ti-gaming-x-slim-16g-graphics-card-nvidia-rtx-4060-ti-16gb-gddr6-memory-18gbps-pcie-4-0-twin-frozr-9-rgb-dlss3/","page_url":"https://nodegrove.io/gpus/rtx-4060-ti-16","notes":null},
    {"gpu_id":"rtx-4070","gpu":"RTX 4070 12 GB","kind":"consumer","maker":"NVIDIA","memory_gb":12,"usable_gb":12,"budget_gb":11.4,"bandwidth_gb_s":504,"spec_url":"https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4070-family/","bandwidth_source_url":"https://www.nvidia.com/en-us/geforce/news/rtx-50-series-graphics-cards-gpu-laptop-announcements/","page_url":"https://nodegrove.io/gpus/rtx-4070","notes":null},
    {"gpu_id":"rtx-3090","gpu":"RTX 3090 24 GB","kind":"consumer","maker":"NVIDIA","memory_gb":24,"usable_gb":24,"budget_gb":22.8,"bandwidth_gb_s":936,"spec_url":"https://www.nvidia.com/en-us/geforce/graphics-cards/30-series/rtx-3090-3090ti/","bandwidth_source_url":"https://www.nvidia.com/content/PDF/nvidia-ampere-ga-102-gpu-architecture-whitepaper-v2.pdf","page_url":"https://nodegrove.io/gpus/rtx-3090","notes":null},
    {"gpu_id":"rtx-4090","gpu":"RTX 4090 24 GB","kind":"consumer","maker":"NVIDIA","memory_gb":24,"usable_gb":24,"budget_gb":22.8,"bandwidth_gb_s":1008,"spec_url":"https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4090/","bandwidth_source_url":"https://images.nvidia.com/aem-dam/Solutions/geforce/ada/nvidia-ada-gpu-architecture.pdf","page_url":"https://nodegrove.io/gpus/rtx-4090","notes":null},
    {"gpu_id":"rtx-5090","gpu":"RTX 5090 32 GB","kind":"consumer","maker":"NVIDIA","memory_gb":32,"usable_gb":32,"budget_gb":30.4,"bandwidth_gb_s":1792,"spec_url":"https://www.nvidia.com/en-us/geforce/graphics-cards/50-series/rtx-5090/","bandwidth_source_url":"https://www.nvidia.com/en-us/geforce/graphics-cards/compare/","page_url":"https://nodegrove.io/gpus/rtx-5090","notes":null},
    {"gpu_id":"rtx-6000-ada","gpu":"RTX 6000 Ada 48 GB","kind":"workstation","maker":"NVIDIA","memory_gb":48,"usable_gb":48,"budget_gb":45.6,"bandwidth_gb_s":960,"spec_url":"https://www.nvidia.com/en-us/design-visualization/rtx-6000/","bandwidth_source_url":"https://www.nvidia.com/content/dam/en-zz/Solutions/design-visualization/rtx-6000/proviz-print-rtx6000-datasheet-web-2504660.pdf","page_url":"https://nodegrove.io/gpus/rtx-6000-ada","notes":null},
    {"gpu_id":"l40s","gpu":"L40S 48 GB","kind":"datacenter","maker":"NVIDIA","memory_gb":48,"usable_gb":48,"budget_gb":45.6,"bandwidth_gb_s":864,"spec_url":"https://www.nvidia.com/en-us/data-center/l40s/","bandwidth_source_url":null,"page_url":"https://nodegrove.io/gpus/l40s","notes":null},
    {"gpu_id":"rtx-pro-6000","gpu":"RTX PRO 6000 Blackwell 96 GB","kind":"workstation","maker":"NVIDIA","memory_gb":96,"usable_gb":96,"budget_gb":91.2,"bandwidth_gb_s":1792,"spec_url":"https://www.nvidia.com/en-us/products/workstations/professional-desktop-gpus/rtx-pro-6000/","bandwidth_source_url":null,"page_url":"https://nodegrove.io/gpus/rtx-pro-6000","notes":null},
    {"gpu_id":"a100-80","gpu":"A100 80 GB","kind":"datacenter","maker":"NVIDIA","memory_gb":80,"usable_gb":80,"budget_gb":76,"bandwidth_gb_s":2039,"spec_url":"https://www.nvidia.com/en-us/data-center/a100/","bandwidth_source_url":null,"page_url":"https://nodegrove.io/gpus/a100-80","notes":null},
    {"gpu_id":"h100-sxm","gpu":"H100 SXM 80 GB","kind":"datacenter","maker":"NVIDIA","memory_gb":80,"usable_gb":80,"budget_gb":76,"bandwidth_gb_s":3350,"spec_url":"https://www.nvidia.com/en-us/data-center/h100/","bandwidth_source_url":null,"page_url":"https://nodegrove.io/gpus/h100-sxm","notes":null},
    {"gpu_id":"m2-ultra","gpu":"Mac Studio M2 Ultra (unified)","kind":"apple","maker":"Apple","memory_gb":192,"usable_gb":144,"budget_gb":136.8,"bandwidth_gb_s":800,"spec_url":"https://support.apple.com/en-us/111835","bandwidth_source_url":null,"page_url":"https://nodegrove.io/gpus/m2-ultra","notes":"Unified memory: the model shares RAM with the system. Up to 192 GB configurable; prompt processing is slower than on NVIDIA."},
    {"gpu_id":"m4-max","gpu":"MacBook Pro M4 Max (unified)","kind":"apple","maker":"Apple","memory_gb":128,"usable_gb":96,"budget_gb":91.2,"bandwidth_gb_s":546,"spec_url":"https://support.apple.com/en-us/121554","bandwidth_source_url":null,"page_url":"https://nodegrove.io/gpus/m4-max","notes":"Unified memory, up to 128 GB configurable."},
    {"gpu_id":"rtx-5060-ti-16","gpu":"RTX 5060 Ti 16 GB","kind":"consumer","maker":"NVIDIA","memory_gb":16,"usable_gb":16,"budget_gb":15.2,"bandwidth_gb_s":448,"spec_url":"https://www.nvidia.com/en-us/geforce/graphics-cards/50-series/rtx-5060-family/","bandwidth_source_url":"https://www.nvidia.com/en-us/geforce/graphics-cards/compare/","page_url":"https://nodegrove.io/gpus/rtx-5060-ti-16","notes":null},
    {"gpu_id":"rx-7900-xtx","gpu":"Radeon RX 7900 XTX 24 GB","kind":"consumer","maker":"AMD","memory_gb":24,"usable_gb":24,"budget_gb":22.8,"bandwidth_gb_s":960,"spec_url":"https://www.amd.com/en/products/graphics/desktops/radeon/7000-series/amd-radeon-rx-7900xtx.html","bandwidth_source_url":null,"page_url":"https://nodegrove.io/gpus/rx-7900-xtx","notes":null},
    {"gpu_id":"dgx-spark","gpu":"DGX Spark 128 GB (unified)","kind":"unified","maker":"NVIDIA","memory_gb":128,"usable_gb":126,"budget_gb":119.7,"bandwidth_gb_s":273,"spec_url":"https://www.nvidia.com/en-us/products/workstations/dgx-spark/","bandwidth_source_url":null,"page_url":"https://nodegrove.io/gpus/dgx-spark","notes":"Unified memory shared with the Arm CPU and DGX OS. A 64 GB version is sold through partners from 23 October 2026."},
    {"gpu_id":"ryzen-ai-max-395","gpu":"Ryzen AI Max+ 395 128 GB (unified)","kind":"unified","maker":"AMD","memory_gb":128,"usable_gb":96,"budget_gb":91.2,"bandwidth_gb_s":256,"spec_url":"https://www.amd.com/en/products/processors/laptop/ryzen/ai-300-series/amd-ryzen-ai-max-plus-395.html","bandwidth_source_url":"https://www.amd.com/en/products/processors/desktops/ryzen/ryzen-ai-halo/ryzen-ai-max-plus-395.html","page_url":"https://nodegrove.io/gpus/ryzen-ai-max-395","notes":"Unified memory shared with the CPU. The power limit (45 to 120 W) is set by the laptop or mini-PC maker, so the same chip performs differently by system."},
    {"gpu_id":"m5-max","gpu":"MacBook Pro M5 Max (unified)","kind":"apple","maker":"Apple","memory_gb":128,"usable_gb":96,"budget_gb":91.2,"bandwidth_gb_s":614,"spec_url":"https://www.apple.com/macbook-pro/specs/","bandwidth_source_url":null,"page_url":"https://nodegrove.io/gpus/m5-max","notes":"Unified memory. 128 GB and 614 GB/s need the 40-core GPU; the 32-core GPU version is 36 GB at 460 GB/s."},
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    {"gpu_id":"m5-ultra","gpu":"Mac Studio M5 Ultra (unified)","kind":"apple","maker":"Apple","memory_gb":512,"usable_gb":384,"budget_gb":364.8,"bandwidth_gb_s":1200,"spec_url":"https://www.apple.com/mac-studio/specs/","bandwidth_source_url":null,"page_url":"https://nodegrove.io/gpus/m5-ultra","notes":"Unified memory. 512 GB needs the 80-core GPU chip and ships in late October 2026; otherwise 96 or 256 GB, at the same 1.2 TB/s."}
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