Mixtral 8x7B needs ~44.3 GB VRAM. Gaudi 3 128GB has 128.0 GB. With Q4_K_M quantization, expect ~186 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
186.3 tok/s
TTFT
1039 ms
Safe context
33K
Memory
44.3 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 | B | Runs well | 173.3 tok/s | 609 ms | 33K |
| Coding | B | Runs well | 186.3 tok/s | 1039 ms | 33K |
| Agentic Coding | B | Runs well | 186.3 tok/s | 1512 ms | 33K |
| Reasoning | B | Runs well | 186.3 tok/s | 1228 ms | 33K |
| RAG | B | Runs well | 186.3 tok/s | 1889 ms | 33K |
How Mixtral 8x7B (47B params) fits at each quantization level on Gaudi 3 128GB (128.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 18.3 GB | Low | C54 |
Q3_K_S | 3 | 23.0 GB | Low | C55 |
NVFP4 | 4 |
Copy-paste commands to run Mixtral 8x7B on your machine.
Run
ollama run mixtralYes, Gaudi 3 128GB can run Mixtral 8x7B with a B grade (Runs well). Expected decode speed: 186.3 tok/s.
Mixtral 8x7B (47B parameters) requires approximately 44.3 GB of memory with Q4_K_M quantization.
The recommended quantization for Mixtral 8x7B is Q4_K_M, which balances quality and memory efficiency.
On Gaudi 3 128GB, Mixtral 8x7B achieves approximately 186.3 tokens per second decode speed with a time-to-first-token of 1039ms using Q4_K_M quantization.
For coding workloads, Mixtral 8x7B on Gaudi 3 128GB receives a B grade with 186.3 tok/s and 33K context.
On Gaudi 3 128GB, Mixtral 8x7B can safely use up to 33K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.
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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/mixtral-8x7b-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:
26.3 GB |
| Medium |
| B55 |
Q4_K_M | 4 | 28.7 GB | Medium | B56 |
Q5_K_M | 5 | 33.8 GB | High | B57 |
Q6_K | 6 | 38.5 GB | High | B57 |
Q8_0 | 8 | 50.3 GB | Very High | B59 |
F16Best for your GPU | 16 | 96.4 GB | Maximum | B63 |
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.