WizardMath 7B needs ~30.6 GB VRAM. NVIDIA DGX Spark 128GB has 0 MB. With F16 quantization, expect ~17 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
41.2 tok/s
TTFT
4695 ms
Safe context
4K
Memory
20.5 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 | F | Too heavy | 6.9 tok/s | 15293 ms | 4K |
| Coding | F | Too heavy | 6.9 tok/s | 28038 ms | 4K |
| Agentic Coding | F | Too heavy | 6.9 tok/s | 40783 ms | 4K |
| Reasoning | F | Too heavy | 6.9 tok/s | 33136 ms | 4K |
| RAG | F | Too heavy | 6.9 tok/s | 50978 ms | 4K |
How WizardMath 7B (7B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | B60 |
Q3_K_S | 3 | 3.4 GB | Low | B60 |
NVFP4 | 4 |
Copy-paste commands to run WizardMath 7B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "WizardLMTeam/WizardMath-7B-V1.1" \
--hf-file "WizardMath-7B-V1.1-Q4_K_M.gguf" \
-c 4096 -ngl 99Upgrade options
NVIDIA H200 141GBBudget pickRaises estimated decode speed by about 138%.
Adds memory headroom for longer context windows and future model growth.
~$30,000 MSRP
NVIDIA H200 PCIe 141GBBest valueRaises estimated decode speed by about 138%.
Adds memory headroom for longer context windows and future model growth.
~$30,000 MSRP
NVIDIA B200 180GBNVIDIA upgradeRaises estimated decode speed by about 138%.
Adds memory headroom for longer context windows and future model growth.
~$30,000 MSRP
Yes, NVIDIA DGX Spark 128GB can run WizardMath 7B at F16 quantization (Runs well). The recommended Q4_K_M requires 7.4 GB which exceeds available memory, but at F16 it needs only 30.6 GB. Expected decode speed: 17.2 tok/s.
WizardMath 7B (7B parameters) requires approximately 7.4 GB at Q4_K_M quantization. On NVIDIA DGX Spark 128GB, it fits at F16 using 30.6 GB.
The recommended quantization is Q4_K_M, but on NVIDIA DGX Spark 128GB the best fitting quantization is F16, which uses 30.6 GB.
On NVIDIA DGX Spark 128GB, WizardMath 7B achieves approximately 17.2 tokens per second decode speed with a time-to-first-token of 11270ms using F16 quantization.
For coding workloads, WizardMath 7B on NVIDIA DGX Spark 128GB receives a F grade with 6.9 tok/s and 4K context.
On NVIDIA DGX Spark 128GB, WizardMath 7B can safely use up to 4K tokens of context at F16 quantization. The model's official context limit is 4K, 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/wizard-math-7b-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:
3.9 GB |
| Medium |
| B60 |
Q4_K_M | 4 | 4.3 GB | Medium | B60 |
Q5_K_M | 5 | 5.0 GB | High | B60 |
Q6_K | 6 | 5.7 GB | High | B60 |
Q8_0 | 8 | 7.5 GB | Very High | B60 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | B61 |
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.