InternLM 7B needs ~26.3 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
8K
Memory
26.3 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 | B | Runs well | 38.4 tok/s | 2753 ms | 8K |
| Coding | B | Runs well | 38.4 tok/s | 5047 ms | 8K |
| Agentic Coding | B | Runs well | 38.4 tok/s | 7341 ms | 8K |
| Reasoning | B | Runs well | 38.4 tok/s | 5964 ms | 8K |
| RAG | B | Runs well | 38.4 tok/s | 9176 ms | 8K |
How InternLM 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 InternLM 7B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "InternLM/InternLM-7B" \
--hf-file "InternLM-7B-Q4_K_M.gguf" \
-c 4096 -ngl 99Upgrade 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 InternLM 7B with a B grade (Runs well). Expected decode speed: 38.4 tok/s.
InternLM 7B (7B parameters) requires approximately 26.3 GB of memory with Q4_K_M quantization.
The recommended quantization for InternLM 7B is Q4_K_M, which balances quality and memory efficiency.
On NVIDIA DGX Spark 128GB, InternLM 7B 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, InternLM 7B on NVIDIA DGX Spark 128GB receives a B grade with 38.4 tok/s and 8K context.
On NVIDIA DGX Spark 128GB, InternLM 7B can safely use up to 8K tokens of context. The model's official context limit is 8K, 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/internlm-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.