Mixtral 8x22B needs ~103.4 GB VRAM. NVIDIA DGX Spark 128GB has 108.8 GB. With Q4_K_M quantization, expect ~4 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 with offload
Decode
4.0 tok/s
TTFT
48916 ms
Safe context
41K
Memory
103.4 GB / 108.8 GB
The model fits in shared memory, but shared-memory bandwidth is now the real limiter.
Fit does not mean dedicated-VRAM speed
Unified or shared memory can make a model technically fit, but sustained tokens per second may still trail a discrete high-bandwidth GPU with less total memory.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
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.
Prioritize bandwidth, not only capacity
If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Tight fit | 4.0 tok/s | 26681 ms | 41K |
| Coding | B | Runs with offload | 4.0 tok/s | 48916 ms | 41K |
| Agentic Coding | B | Runs with offload | 4.0 tok/s | 71150 ms | 41K |
| Reasoning | B | Runs with offload | 4.0 tok/s | 57809 ms | 41K |
| RAG | B | Runs with offload | 4.0 tok/s | 88937 ms | 41K |
Inference speed
Estimated decode speed (tokens/sec) for Mixtral 8x22B 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 ~14 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 | 13.5 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 9.2 | Heavy offload |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 8.7 | Heavy offload |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 6.8 | Heavy offload |
2× RX 7900 XTX 24GB | 48 GB | Q4_K_M | 5.0 | Too big |
 RTX 5090 32GB | 32 GB | Q4_K_M | 3.9 | Too big |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 3.7 | Too big |
| 2× RTX 4090 24GB | 48 GB | Q4_K_M | 3.4 | Too big |
| 2× RTX 3090 24GB | 48 GB | Q4_K_M | 2.9 | Too big |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 2.6 | Too big |
| 4× RTX 3060 12GB | 48 GB | Q4_K_M | 2.6 | Too big |
| RTX 4090 24GB | 24 GB | Q4_K_M | 2.5 | Too big |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 2.5 | Too big |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 2.4 | Too big |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 2.3 | Too big |
| RTX 3090 24GB | 24 GB | Q4_K_M | 2.1 | Too big |
| RTX 4080 Super 16GB | 16 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 |
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.
How Mixtral 8x22B (141B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 55.0 GB | Low | B61 |
Q3_K_SBest for your GPU | 3 | 69.1 GB | Low | B61 |
NVFP4 | 4 | 79.0 GB | Medium | F0 |
Q4_K_M | 4 | 86.0 GB | Medium | F0 |
Q5_K_M | 5 | 101.5 GB | High | F0 |
Q6_K | 6 | 115.6 GB | High | F0 |
Q8_0 | 8 | 150.9 GB | Very High | F0 |
F16 | 16 | 289.0 GB | Maximum | F0 |
Copy-paste commands to run Mixtral 8x22B on your machine.
Run
ollama run mixtral:8x22bUpgrade-Optionen
NVIDIA H200 141GBGünstige WahlRaises estimated decode speed by about 2335%.
Adds memory headroom for longer context windows and future model growth.
ca. $30,000 MSRP
NVIDIA H200 PCIe 141GBBestes Preis-Leistungs-VerhältnisRaises estimated decode speed by about 2335%.
Adds memory headroom for longer context windows and future model growth.
ca. $30,000 MSRP
NVIDIA B200 180GBNVIDIA-UpgradeRaises estimated decode speed by about 3960%.
Adds memory headroom for longer context windows and future model growth.
ca. $30,000 MSRP
Yes, NVIDIA DGX Spark 128GB can run Mixtral 8x22B with a B grade (Runs with offload). Expected decode speed: 4.0 tok/s.
Mixtral 8x22B (141B parameters) requires approximately 103.4 GB of memory with Q4_K_M quantization.
The recommended quantization for Mixtral 8x22B is Q4_K_M, which balances quality and memory efficiency.
On NVIDIA DGX Spark 128GB, Mixtral 8x22B achieves approximately 4.0 tokens per second decode speed with a time-to-first-token of 48916ms using Q4_K_M quantization.
For coding workloads, Mixtral 8x22B on NVIDIA DGX Spark 128GB receives a B grade with 4.0 tok/s and 41K context.
On NVIDIA DGX Spark 128GB, Mixtral 8x22B can safely use up to 41K tokens of context. The model's official context limit is 66K, but available memory constrains the safe maximum.
Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/mixtral-8x22b-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: