internlm2 math plus 7b IMat needs ~19.3 GB VRAM. NVIDIA DGX Spark 128GB has 108.8 GB. With Q4_K_M quantization, expect ~38 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
38.4 tok/s
TTFT
5047 ms
Safe context
1.8M
Memory
19.3 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 | C | Runs well | 38.4 tok/s | 2753 ms | 1.8M |
| Coding | C | Runs well | 38.4 tok/s | 5047 ms | 1.8M |
| Agentic Coding | C | Runs well | 38.4 tok/s | 7341 ms | 1.8M |
| Reasoning | C | Runs well | 38.4 tok/s | 5964 ms | 1.8M |
| RAG | C | Runs well | 38.4 tok/s | 9176 ms | 1.8M |
How internlm2 math plus 7b IMat (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 | D39 |
Q3_K_S | 3 | 3.4 GB | Low | D39 |
NVFP4 | 4 |
Copy-paste commands to run internlm2 math plus 7b IMat on your machine.
Run
lms load hf-legraphista--internlm2-math-plus-7b-imat-gguf && lms server startUpgrade options
NVIDIA H200 141GBBudget pickRaises estimated decode speed by about 155%.
Adds memory headroom for longer context windows and future model growth.
~$30,000 MSRP
NVIDIA H200 PCIe 141GBBest valueRaises estimated decode speed by about 155%.
Adds memory headroom for longer context windows and future model growth.
~$30,000 MSRP
NVIDIA B200 180GBNVIDIA upgradeRaises estimated decode speed by about 155%.
Adds memory headroom for longer context windows and future model growth.
~$30,000 MSRP
Yes, NVIDIA DGX Spark 128GB can run internlm2 math plus 7b IMat with a C grade (Runs well). Expected decode speed: 38.4 tok/s.
internlm2 math plus 7b IMat (7B parameters) requires approximately 19.3 GB of memory with Q4_K_M quantization.
The recommended quantization for internlm2 math plus 7b IMat is Q4_K_M, which balances quality and memory efficiency.
On NVIDIA DGX Spark 128GB, internlm2 math plus 7b IMat achieves approximately 38.4 tokens per second decode speed with a time-to-first-token of 5047ms using Q4_K_M quantization.
For coding workloads, internlm2 math plus 7b IMat on NVIDIA DGX Spark 128GB receives a C grade with 38.4 tok/s and 1.8M context.
On NVIDIA DGX Spark 128GB, internlm2 math plus 7b IMat can safely use up to 1.8M tokens of context. The model's official context limit is —, 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/hf-legraphista--internlm2-math-plus-7b-imat-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:
3.9 GB |
| Medium |
| D39 |
Q4_K_M | 4 | 4.3 GB | Medium | D39 |
Q5_K_M | 5 | 5.0 GB | High | D39 |
Q6_K | 6 | 5.7 GB | High | D39 |
Q8_0 | 8 | 7.5 GB | Very High | D39 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | D40 |
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.