Mistral
Mistral Small 4 119B (119B parameters) requires approximately 79.5 GB of VRAM with Q4_K_M quantization. As a Mixture of Experts model with 6.5B active parameters, it uses less memory than its total parameter count suggests. For the best balance of quality and speed, we recommend hardware with at least 92 GB of VRAM.
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— copy & paste to run locallyCopy-paste commands to run Mistral Small 4 119B on your machine.
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lms load Mistral-Small-4-119B-2603 && lms server startQuick specs
About this model
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Inference speed
Estimated decode speed (tokens/sec) for Mistral Small 4 119B at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is Mac Studio M3 Ultra 256GB at ~38 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 37.6 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 30.8 | Offloads |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 29.3 | Offloads |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 22.9 | Offloads |
2× RX 7900 XTX 24GB | 48 GB | Q4_K_M | 11.9 | Too big |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 10.7 | Too big |
 2× RTX 4090 24GB | 48 GB | Q4_K_M | 8.0 | Too big |
| RTX 5090 32GB | 32 GB | Q4_K_M | 7.9 | Too big |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 7.5 | Too big |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 6.8 | Too big |
| 2× RTX 3090 24GB | 48 GB | Q4_K_M | 6.8 | Too big |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 6.4 | Too big |
| 4× RTX 3060 12GB | 48 GB | Q4_K_M | 6.0 | Too big |
| RTX 4090 24GB | 24 GB | Q4_K_M | 5.0 | Too big |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 4.5 | Too big |
| RTX 3090 24GB | 24 GB | Q4_K_M | 4.3 | Too big |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 4.0 | Too big |
| RTX 4070 12GB | 12 GB | Q4_K_M | 2.5 | Too big |
| RTX 3060 12GB | 12 GB | Q4_K_M | 2.0 | Too big |
| RTX 4060 8GB | 8 GB | Q4_K_M | 2.0 | Too big |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
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Quantization
How much VRAM Mistral Small 4 119B (119B) needs at each GGUF quant, and whether it fits a 24 GB card (RTX 4090 / 3090). The recommended Q4_K_M uses ~72.6 GB — about 43% less VRAM than Q8_0, at a small quality cost.
| Quant | Bits | VRAM (weights) | Quality | Fits 24 GB? |
|---|---|---|---|---|
| Q2_K | 2 | 46.4 GB | Low | Too big |
| Q3_K_S | 3 | 58.3 GB | Low | Too big |
| NVFP4 | 4 | 66.6 GB | Medium | Too big |
| Q4_K_Mrecommended | 4 | 72.6 GB | Medium | Too big |
| Q5_K_M | 5 | 85.7 GB | High | Too big |
| Q6_K | 6 | 97.6 GB | High | Too big |
| Q8_0 | 8 | 127.3 GB | Very High | Too big |
| F16 | 16 | 244 GB | Maximum | Too big |
VRAM shown is quantized weights only; add ~1–3 GB runtime overhead plus KV cache for your context length. Lower quants trade quality for memory — Q4_K_M is the usual sweet spot; Q2/Q3 only when you must fit a bigger model.
Quality benchmarks
Coding
Reasoning
Source: official · 2026-03-16
Hardware compatibility
Computing compatibility...
Memory breakdown
Frequently asked questions
Mistral Small 4 119B (119B parameters) requires approximately 79.5 GB of VRAM with Q4_K_M quantization. Lower quantizations like Q4_K_M use less memory but may reduce quality.
Yes, Mac Studio M3 Ultra 256GB can run Mistral Small 4 119B with a compatibility score of 92/100. It provides 256 GB of memory and achieves approximately 37.6 tokens per second.
The recommended quantization for Mistral Small 4 119B is Q4_K_M, which offers the best balance between model quality and memory efficiency. Higher quantizations preserve more quality but require more VRAM.
The top recommended hardware for Mistral Small 4 119B: AMD Instinct MI250X 128GB (score: 97/100), AMD Instinct MI300A 128GB (score: 97/100), AMD Instinct MI250 128GB (score: 97/100). These provide the best combination of memory, bandwidth, and compute for running this model locally.
Yes, Mistral Small 4 119B is well-suited for chat as well as coding, reasoning, vision. It was designed with these use cases in mind.
See also
