Granite 4.2 8B
Granite 4.2 8B needs 7.2 GB of VRAM at Q4_K_M with 8k context, computed from its config.json as checked on 16 October 2026. The smallest card that holds it has 8 GB, a tight fit, and the fastest desktop card that runs it, the RTX 5090, generates at most 246 tokens per second.
- Parameters
- 8.8B
- Context
- 131,072 tokens
- Licence
- Apache 2.0
- Released
- August 2026
- Config checked
- 16 October 2026
Open Granite 4.2 8B in the LLM checker
Which card size runs it
7.2 GB against each memory size. A model may use 95% of a card, and only what is left over, or missing, is coloured; tight means above 85%, with little room for a longer conversation.
Other quantisations, context lengths or a Q8 KV cache: the VRAM calculator.
Computed 16 October 2026Which GPU to pick
The smallest card here that loads it at Q4_K_M with 8k of context is the RTX 3060 (12 GB): 7.2 GB needed, an estimated 49 tokens per second. That is only the smallest card with its own page here: the model itself fits any 8 GB card. For room to work, meaning 32,768 tokens of context while staying under 85% of memory, step up to the RTX 4060 Ti (16 GB), which needs 11.2 GB for that and generates at about 39 tokens per second.
At Q8_0, which is near-lossless, the card is the RTX 4060 Ti (16 GB), needing 11.5 GB. On Apple silicon, a Mac mini M4 Pro holds it with 32,768 tokens of context at an estimated 37 tokens per second; prompt processing is slower than on Nvidia, which long documents make obvious. Among unified-memory desktops, the Ryzen AI Max+ 395 holds it at 32,768 tokens in 11.2 GB, at an estimated 35 tokens per second.
Speed on each card
| GPU | Fits | Est. tokens/s | Feels like | Memory | Bandwidth |
|---|---|---|---|---|---|
| H100 SXM | Yes | ≤ 459 | faster than you can read | 80 GB | 3,350 GB/s |
| A100 | Yes | ≤ 280 | faster than you can read | 80 GB | 2,039 GB/s |
| RTX 5090 | Yes | ≤ 246 | faster than you can read | 32 GB | 1,792 GB/s |
| RTX PRO 6000 Blackwell | Yes | ≤ 246 | faster than you can read | 96 GB | 1,792 GB/s |
| RTX PRO 5000 Blackwell | Yes | ≤ 184 | faster than you can read | 48 GB | 1,344 GB/s |
| RTX 4090 | Yes | ≤ 138 | faster than you can read | 24 GB | 1,008 GB/s |
| RTX 5080 | Yes | ≤ 132 | faster than you can read | 16 GB | 960 GB/s |
| RX 7900 XTX | Yes | ≤ 132 | faster than you can read | 24 GB | 960 GB/s |
| RTX 6000 Ada | Yes | ≤ 132 | faster than you can read | 48 GB | 960 GB/s |
| RTX 3090 | Yes | ≤ 128 | faster than you can read | 24 GB | 936 GB/s |
| RTX 5070 Ti | Yes | ≤ 123 | faster than you can read | 16 GB | 896 GB/s |
| RTX PRO 4500 Blackwell | Yes | ≤ 123 | faster than you can read | 32 GB | 896 GB/s |
| L40S | Yes | ≤ 118 | faster than you can read | 48 GB | 864 GB/s |
| RX 7900 XT | Yes | ≤ 110 | faster than you can read | 20 GB | 800 GB/s |
| RTX A6000 | Yes | ≤ 105 | faster than you can read | 48 GB | 768 GB/s |
| RTX 4080 SUPER | Yes | ≤ 101 | faster than you can read | 16 GB | 736 GB/s |
| RTX 5070 | Yes | 92 | faster than you can read | 12 GB | 672 GB/s |
| RTX PRO 4000 Blackwell | Yes | 92 | faster than you can read | 24 GB | 672 GB/s |
| RX 9070 XT | Yes | 88 | faster than you can read | 16 GB | 640 GB/s |
| Radeon AI PRO R9700 | Yes | 88 | faster than you can read | 32 GB | 640 GB/s |
| Arc Pro B70 | Yes | 83 | faster than you can read | 32 GB | 608 GB/s |
| RTX 4070 | Yes | 69 | faster than you can read | 12 GB | 504 GB/s |
| Arc B580 | Yes | 63 | faster than you can read | 12 GB | 456 GB/s |
| Arc Pro B60 | Yes | 63 | faster than you can read | 24 GB | 456 GB/s |
| RTX 5060 Ti | Yes | 61 | faster than you can read | 16 GB | 448 GB/s |
| RTX PRO 4000 Blackwell SFF | Yes | 59 | comfortable for chat | 24 GB | 432 GB/s |
| RTX 3060 | Yes | 49 | comfortable for chat | 12 GB | 360 GB/s |
| RX 9060 XT | Yes | 44 | comfortable for chat | 16 GB | 320 GB/s |
| RTX Spark | Yes | 41 | comfortable for chat | 128 GB (102 for the GPU) | 300 GB/s |
| RTX 4060 Ti | Yes | 39 | comfortable for chat | 16 GB | 288 GB/s |
| DGX Spark | Yes | 37 | comfortable for chat | 128 GB (126 for the GPU) | 273 GB/s |
| Ryzen AI Max+ 395 | Yes | 35 | comfortable for chat | 128 GB (96 for the GPU) | 256 GB/s |
Single-stream decode, from memory bandwidth at 70% efficiency. Prompt processing and batching not included. These are ceilings, not measurements: Granite 4.2 8B reads only 5.1 GB of weights per token at Q4, so little that the per-token costs the formula leaves out decide much of the real speed. The 16 figures marked ≤ are where a real runtime falls furthest below the number shown. See the speed estimator for other quantisations and Apple hardware, or every card compared if you are choosing hardware rather than a model.
Computed 16 October 2026Memory by quantisation and context
| Quant | 4,096 ctx | 8,192 ctx | 32,768 ctx |
|---|---|---|---|
| Q8_0 | 10.9 GB · fits 12 GB | 11.5 GB · fits 16 GB | 15.6 GB · fits 24 GB |
| Q5_K_M | 7.7 GB · fits 12 GB | 8.3 GB · fits 12 GB | 12.4 GB · fits 16 GB |
| Q4_K_M | 6.5 GB · fits 8 GB | 7.2 GB · fits 8 GB | 11.2 GB · fits 12 GB |
Weights at this quant: 5.1 GB at Q4. Every extra 1,000 tokens of context adds about 0.164 GB of KV cache at FP16. Try other settings in the VRAM calculator.
About the model
Granite 4.2 8B has 8.8B parameters, 40 layers and 8 KV heads of dimension 128. The middle size of IBM's Granite 4.2 family, August 2026: a dense text model that thinks before it answers, with tool calling in its chat template. Granite is IBM's line for business use, and every size is Apache 2.0.
Text only 131,072-token window Apache 2.0 August 2026
Reasoning. Full thinking by default; a non-thinking mode and a shorter low-effort mode are set per request.
Native context window: 131,072 tokens. Licence: Apache 2.0, a permissive licence. Weights published in August 2026.
Good at
- Tool calling: the chat template renders tool definitions, and IBM's training data includes agentic traces.
- Chat and coding on a 12 GB card at a modest context.
- Twelve tested languages, Japanese among them.
- Apache 2.0, with no field-of-use conditions.
Watch out for
- Every layer keeps a full-length cache, so long context costs several times what a 2026 hybrid model pays for the same conversation.
- Thinking is on by default, and thinking tokens fill the context like any others. Turn it off, or use the low-effort mode, for quick questions.
- IBM advertises 512K, but that is a long-context extension: the native window is 131,072 tokens, which is what this site counts.
Similar models
The models that need about the same memory as Granite 4.2 8B at Q4_K_M and 8k, which makes them the real alternatives on whatever card you have. The context column is what each extra 1,000 tokens costs, and it is where models of the same size differ most.
| Model | Size | Needs | Per 1k context | Window | Released |
|---|---|---|---|---|---|
| Granite 4.2 8B | 8.8B | 7.2 GB | 0.164 GB | 128k | 2026-08 |
| Qwen3.5 9B | 9.7B | 6.7 GB | 0.033 GB | 256k | 2026-02 |
| Qwen3 8B | 8.2B | 6.7 GB | 0.147 GB | 40k | 2025-04 |
| Llama 3.1 8B | 8B | 6.4 GB | 0.131 GB | 128k | 2024-07 |
| Gemma 4 12B | 12B | 8.2 GB | 0.016 GB | 256k | 2026-05 |
See every model compared, which models fit on 8 to 96 GB, or the cheapest hour of every cloud card.
Questions
- Can I run Granite 4.2 8B on a 24 GB card?
- Yes, at Q4_K_M and 8k context it needs about 7.2 GB, which fits a 24 GB card with room.
- How much VRAM does Granite 4.2 8B need at Q8?
- About 11.5 GB at 8k context, or 15.6 GB at 32k. Q8 is near-lossless; use it when it fits.
- What is the context length of Granite 4.2 8B?
- 131,072 tokens natively. Holding all of it at Q4_K_M takes about 27.3 GB, of which 21.5 GB is context.
- How fast is Granite 4.2 8B on an RTX 4090?
- At most about 138 tokens per second at Q4, single stream, which is faster than you can read. That is a ceiling worked out from the card's memory bandwidth, not a measurement, and at a figure this high real runtimes land well below it, because the costs the formula leaves out take a growing share of each token.
- Architecture values from config.json in ibm-granite/granite-4.2-8b on Hugging Face, checked 16 October 2026. Every figure on this page is computed from those values; check them before buying hardware for this model.
- Estimates assume a single conversation on a card that is otherwise free. A desktop on the same GPU takes 0.5 to 2 GB.