InternLM Chat 7B needs ~36.4 GB VRAM. NVIDIA DGX Spark 128GB has 0 MB. With F16 quantization, expect ~16 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 | F | Too heavy | 6.9 tok/s | 28038 ms | 4K |
| 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 Chat 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 | 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 |
Copy-paste commands to run InternLM Chat 7B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "InternLM/InternLM-Chat-7B" \
--hf-file "InternLM-Chat-7B-Q4_K_M.gguf" \
-c 4096 -ngl 99Opções de upgrade
NVIDIA H200 141GBOpção econômicaRaises estimated decode speed by about 155%.
Adds memory headroom for longer context windows and future model growth.
~$30,000 MSRP
NVIDIA H200 PCIe 141GBMelhor custo-benefícioRaises estimated decode speed by about 155%.
Adds memory headroom for longer context windows and future model growth.
~$30,000 MSRP
NVIDIA B200 180GBUpgrade NVIDIARaises 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 Chat 7B at F16 quantization (Runs well). The recommended Q4_K_M requires 13.3 GB which exceeds available memory, but at F16 it needs only 36.4 GB. Expected decode speed: 16.0 tok/s.
InternLM Chat 7B (7B parameters) requires approximately 13.3 GB at Q4_K_M quantization. On NVIDIA DGX Spark 128GB, it fits at F16 using 36.4 GB.
The recommended quantization is Q4_K_M, but on NVIDIA DGX Spark 128GB the best fitting quantization is F16, which uses 36.4 GB.
On NVIDIA DGX Spark 128GB, InternLM Chat 7B achieves approximately 16.0 tokens per second decode speed with a time-to-first-token of 12115ms using F16 quantization.
For coding workloads, InternLM Chat 7B on NVIDIA DGX Spark 128GB receives a F grade with 6.9 tok/s and 4K context.
On NVIDIA DGX Spark 128GB, InternLM Chat 7B can safely use up to 8K tokens of context at F16 quantization. The model's official context limit is 8K, but available memory constrains the safe maximum.
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/internlm-chat-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>
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