Devstral 2 123B Instruct needs ~94.1 GB VRAM. Gaudi 3 128GB has 128.0 GB. With Q4_K_M quantization, expect ~38 tok/s.
Operating mode
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
Fit status
Runs well
Decode
37.5 tok/s
TTFT
5157 ms
Safe context
117K
Memory
94.1 GB / 128.0 GB
The raw memory story may look fine, but the software ecosystem is still a constraint here.
Runtime ecosystem is narrower than CUDA
Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.
Prefer CUDA if you want the path of least resistance
If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | S | Runs well | 37.5 tok/s | 2813 ms | 117K |
| Coding | S | Runs well | 37.5 tok/s | 5157 ms | 117K |
| Agentic Coding | S | Runs well | 37.5 tok/s | 7501 ms | 117K |
| Reasoning | S | Runs well | 37.5 tok/s | 6095 ms | 117K |
| RAG | S | Runs well | 37.5 tok/s | 9377 ms | 117K |
How Devstral 2 123B Instruct (123B params) fits at each quantization level on Gaudi 3 128GB (128.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 48.0 GB | Low | S88 |
Q3_K_S | 3 | 60.3 GB | Low | S90 |
NVFP4 | 4 |
Copy-paste commands to run Devstral 2 123B Instruct on your machine.
Run
lms load Devstral-2-123B-Instruct-2512 && lms server startYes, Gaudi 3 128GB can run Devstral 2 123B Instruct with a S grade (Runs well). Expected decode speed: 37.5 tok/s.
Devstral 2 123B Instruct (123B parameters) requires approximately 94.1 GB of memory with Q4_K_M quantization.
The recommended quantization for Devstral 2 123B Instruct is Q4_K_M, which balances quality and memory efficiency.
On Gaudi 3 128GB, Devstral 2 123B Instruct achieves approximately 37.5 tokens per second decode speed with a time-to-first-token of 5157ms using Q4_K_M quantization.
For coding workloads, Devstral 2 123B Instruct on Gaudi 3 128GB receives a S grade with 37.5 tok/s and 117K context.
On Gaudi 3 128GB, Devstral 2 123B Instruct can safely use up to 117K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/devstral-2-123b-on-gaudi-3-128gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
68.9 GB |
| Medium |
| S91 |
Q4_K_M | 4 | 75.0 GB | Medium | S91 |
Q5_K_M | 5 | 88.6 GB | High | S91 |
Q6_KBest for your GPU | 6 | 100.9 GB | High | S91 |
Q8_0 | 8 | 131.6 GB | Very High | F0 |
F16 | 16 | 252.2 GB | Maximum | F0 |
Prefer CUDA if you want the path of least resistance. If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.