mistral small 3.1 24b instruct 2503 hf needs ~66.3 GB VRAM. NVIDIA DGX Spark 128GB has 0 MB. With F16 quantization, expect ~5 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
11.2 tok/s
TTFT
17303 ms
Safe context
455K
Memory
31.7 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.
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 11.2 tok/s | 9438 ms | 455K |
| Coding | F | Too heavy | 2.0 tok/s | 96130 ms | 4K |
| Agentic Coding | C | Runs well | 11.2 tok/s | 25169 ms | 455K |
| Reasoning | C | Runs well | 11.2 tok/s | 20450 ms | 455K |
| RAG | C | Runs well | 11.2 tok/s | 31461 ms | 455K |
How mistral small 3.1 24b instruct 2503 hf (24B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 9.4 GB | Low | D40 |
Q3_K_S | 3 | 11.8 GB | Low | D40 |
NVFP4 | 4 |
Copy-paste commands to run mistral small 3.1 24b instruct 2503 hf on your machine.
Run
lms load hf-maziyarpanahi--mistral-small-3-1-24b-instruct-2503-hf-gguf && lms server startUpgrade options
NVIDIA H200 141GBBudget pickRaises estimated decode speed by about 2359%.
Adds memory headroom for longer context windows and future model growth.
~$30,000 MSRP
NVIDIA H200 PCIe 141GBBest valueRaises estimated decode speed by about 2359%.
Adds memory headroom for longer context windows and future model growth.
~$30,000 MSRP
NVIDIA B200 180GBNVIDIA upgradeRaises estimated decode speed by about 2900%.
Adds memory headroom for longer context windows and future model growth.
~$30,000 MSRP
Yes, NVIDIA DGX Spark 128GB can run mistral small 3.1 24b instruct 2503 hf at F16 quantization (Runs well). The recommended Q4_K_M requires 18.7 GB which exceeds available memory, but at F16 it needs only 66.3 GB. Expected decode speed: 4.7 tok/s.
mistral small 3.1 24b instruct 2503 hf (24B parameters) requires approximately 18.7 GB at Q4_K_M quantization. On NVIDIA DGX Spark 128GB, it fits at F16 using 66.3 GB.
The recommended quantization is Q4_K_M, but on NVIDIA DGX Spark 128GB the best fitting quantization is F16, which uses 66.3 GB.
On NVIDIA DGX Spark 128GB, mistral small 3.1 24b instruct 2503 hf achieves approximately 4.7 tokens per second decode speed with a time-to-first-token of 41536ms using F16 quantization.
For coding workloads, mistral small 3.1 24b instruct 2503 hf on NVIDIA DGX Spark 128GB receives a F grade with 2.0 tok/s and 4K context.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-maziyarpanahi--mistral-small-3-1-24b-instruct-2503-hf-gguf-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:
13.4 GB |
| Medium |
| D40 |
Q4_K_M | 4 | 14.6 GB | Medium | C40 |
Q5_K_M | 5 | 17.3 GB | High | C40 |
Q6_K | 6 | 19.7 GB | High | C41 |
Q8_0 | 8 | 25.7 GB | Very High | C42 |
F16Best for your GPU | 16 | 49.2 GB | Maximum | C47 |
On NVIDIA DGX Spark 128GB, mistral small 3.1 24b instruct 2503 hf can safely use up to 258K tokens of context at F16 quantization. The model's official context limit is —, 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.