Mistral
Devstral 2 123B Instruct (123B parameters) requires approximately 81.9 GB of VRAM with Q4_K_M quantization. For the best balance of quality and speed, we recommend hardware with at least 95 GB of VRAM.
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— copy & paste to run locallyCopy-paste commands to run Devstral 2 123B Instruct on your machine.
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lms load Devstral-2-123B-Instruct-2512 && lms server startQuick specs
About this model
Inference speed
Estimated decode speed (tokens/sec) for Devstral 2 123B Instruct at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is MacBook Pro M4 Max 128GB at ~8 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? |
|---|---|---|---|---|
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 8.2 | Offloads |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 8.1 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 6.3 | Offloads |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 6.0 | Offloads |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 3.9 | Too big |
2× RX 7900 XTX 24GB | 48 GB | Q4_K_M | 3.7 | Too big |
 2× RTX 4090 24GB | 48 GB | Q4_K_M | 2.6 | Too big |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 2.5 | Too big |
| 2× RTX 3090 24GB | 48 GB | Q4_K_M | 2.2 | Too big |
| RTX 5090 32GB | 32 GB | Q4_K_M | 2.0 | Too big |
| RTX 4090 24GB | 24 GB | Q4_K_M | 2.0 | Too big |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 2.0 | Too big |
| RTX 3090 24GB | 24 GB | Q4_K_M | 2.0 | Too big |
| RTX 4070 12GB | 12 GB | Q4_K_M | 2.0 | 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 |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 2.0 | Too big |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 2.0 | Too big |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 2.0 | Too big |
| 4× RTX 3060 12GB | 48 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 Devstral 2 123B Instruct (123B) needs at each GGUF quant, and whether it fits a 24 GB card (RTX 4090 / 3090). The recommended Q4_K_M uses ~75 GB — about 43% less VRAM than Q8_0, at a small quality cost.
| Quant | Bits | VRAM (weights) | Quality | Fits 24 GB? |
|---|---|---|---|---|
| Q2_K | 2 | 48 GB | Low | Too big |
| Q3_K_S | 3 | 60.3 GB | Low | Too big |
| NVFP4 | 4 | 68.9 GB | Medium | Too big |
| Q4_K_Mrecommended | 4 | 75 GB | Medium | Too big |
| Q5_K_M | 5 | 88.6 GB | High | Too big |
| Q6_K | 6 | 100.9 GB | High | Too big |
| Q8_0 | 8 | 131.6 GB | Very High | Too big |
| F16 | 16 | 252.2 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
Source: official · 2025-12-18
Hardware compatibility
Computing compatibility...
Memory breakdown
Frequently asked questions
Devstral 2 123B Instruct (123B parameters) requires approximately 81.9 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 Devstral 2 123B Instruct with a compatibility score of 90/100. It provides 256 GB of memory and achieves approximately 8.1 tokens per second.
The recommended quantization for Devstral 2 123B Instruct 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 Devstral 2 123B Instruct: AMD Instinct MI300A 128GB (score: 98/100), NVIDIA H200 141GB (score: 98/100), NVIDIA H200 PCIe 141GB (score: 98/100). These provide the best combination of memory, bandwidth, and compute for running this model locally.
Yes, Devstral 2 123B Instruct is well-suited for coding as well as reasoning, agentic, vision. It was designed with these use cases in mind.
See also
