Mixtral 8x7B needs ~44.9 GB VRAM. NVIDIA DGX Spark 128GB has 108.8 GB. With Q4_K_M quantization, expect ~12 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
11.8 tok/s
TTFT
16432 ms
Safe context
33K
Memory
44.9 GB / 108.8 GB
This setup is broadly balanced for this model.
Shared-memory contention still exists
The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 11.8 tok/s | 8963 ms | 33K |
| Coding | B | Runs well | 11.8 tok/s | 16432 ms | 33K |
| Agentic Coding | B | Runs well | 11.8 tok/s | 23900 ms | 33K |
| Reasoning | B | Runs well | 11.8 tok/s | 19419 ms | 33K |
| RAG | B | Runs well | 11.8 tok/s | 29876 ms | 33K |
How Mixtral 8x7B (47B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 18.3 GB | Low | B56 |
Q3_K_S | 3 | 23.0 GB | Low | B57 |
NVFP4 | 4 |
Copy-paste commands to run Mixtral 8x7B on your machine.
Run
ollama run mixtralUpgrade options
NVIDIA H200 141GBBudget pickRaises estimated decode speed by about 2358%.
Adds memory headroom for longer context windows and future model growth.
~$30,000 MSRP
NVIDIA H200 PCIe 141GBBest valueRaises estimated decode speed by about 2358%.
Adds memory headroom for longer context windows and future model growth.
~$30,000 MSRP
NVIDIA B200 180GBNVIDIA upgradeRaises estimated decode speed by about 3997%.
Adds memory headroom for longer context windows and future model growth.
~$30,000 MSRP
Yes, NVIDIA DGX Spark 128GB can run Mixtral 8x7B with a B grade (Runs well). Expected decode speed: 11.8 tok/s.
Mixtral 8x7B (47B parameters) requires approximately 44.9 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 NVIDIA DGX Spark 128GB, Mixtral 8x7B achieves approximately 11.8 tokens per second decode speed with a time-to-first-token of 16432ms using Q4_K_M quantization.
For coding workloads, Mixtral 8x7B on NVIDIA DGX Spark 128GB receives a B grade with 11.8 tok/s and 33K context.
On NVIDIA DGX Spark 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/mixtral-8x7b-on-dgx-spark-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 |
| B57 |
Q4_K_M | 4 | 28.7 GB | Medium | B58 |
Q5_K_M | 5 | 33.8 GB | High | B59 |
Q6_K | 6 | 38.5 GB | High | B60 |
Q8_0Best for your GPU | 8 | 50.3 GB | Very High | B63 |
F16 | 16 | 96.4 GB | Maximum | F0 |
Not always. NVIDIA DGX Spark 128GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.