internlm2 5 20b chat needs ~28.8 GB VRAM. NVIDIA DGX Spark 128GB has 108.8 GB. With Q4_K_M quantization, expect ~13 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
13.4 tok/s
TTFT
14420 ms
Safe context
562K
Memory
28.8 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 | 13.4 tok/s | 7865 ms | 562K |
| Coding | C | Runs well | 13.4 tok/s | 14420 ms | 562K |
| Agentic Coding | C | Runs well | 13.4 tok/s | 20974 ms | 562K |
| Reasoning | C | Runs well | 13.4 tok/s | 17041 ms | 562K |
| RAG | C | Runs well | 13.4 tok/s | 26217 ms | 562K |
How internlm2 5 20b chat (20B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 7.8 GB | Low | D39 |
Q3_K_S | 3 | 9.8 GB | Low | D39 |
NVFP4 | 4 | 11.2 GB | Medium | D39 |
Q4_K_M | 4 | 12.2 GB | Medium | D39 |
Q5_K_M | 5 | 14.4 GB | High | D40 |
Q6_K | 6 | 16.4 GB | High | D40 |
Q8_0 | 8 | 21.4 GB | Very High | C41 |
F16Best for your GPU | 16 | 41.0 GB | Maximum | C45 |
Copy-paste commands to run internlm2 5 20b chat on your machine.
Run
lms load hf-bartowski--internlm2-5-20b-chat-gguf && lms server startUpgrade-Optionen
NVIDIA H200 141GBGünstige WahlRaises estimated decode speed by about 1990%.
Adds memory headroom for longer context windows and future model growth.
ca. $30,000 MSRP
NVIDIA H200 PCIe 141GBBestes Preis-Leistungs-VerhältnisRaises estimated decode speed by about 1990%.
Adds memory headroom for longer context windows and future model growth.
ca. $30,000 MSRP
NVIDIA B200 180GBNVIDIA-UpgradeRaises estimated decode speed by about 1990%.
Adds memory headroom for longer context windows and future model growth.
ca. $30,000 MSRP
Yes, NVIDIA DGX Spark 128GB can run internlm2 5 20b chat with a C grade (Runs well). Expected decode speed: 13.4 tok/s.
internlm2 5 20b chat (20B parameters) requires approximately 28.8 GB of memory with Q4_K_M quantization.
The recommended quantization for internlm2 5 20b chat is Q4_K_M, which balances quality and memory efficiency.
On NVIDIA DGX Spark 128GB, internlm2 5 20b chat achieves approximately 13.4 tokens per second decode speed with a time-to-first-token of 14420ms using Q4_K_M quantization.
For coding workloads, internlm2 5 20b chat on NVIDIA DGX Spark 128GB receives a C grade with 13.4 tok/s and 562K context.
On NVIDIA DGX Spark 128GB, internlm2 5 20b chat can safely use up to 562K tokens of context. The model's official context limit is —, 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.
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<iframe src="https://willitrunai.com/embed/hf-bartowski--internlm2-5-20b-chat-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>
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