internlm2 limarp chat 20b needs ~28.2 GB VRAM. Intel Data Center GPU Max 1550 128GB has 128.0 GB. With Q4_K_M quantization, expect ~165 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
165.2 tok/s
TTFT
1172 ms
Safe context
697K
Memory
28.2 GB / 128.0 GB
The raw memory story may look fine, but the software ecosystem is still a constraint here.
Runtime ecosystem is narrower than CUDA
Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.
Prefer CUDA if you want the path of least resistance
If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 165.2 tok/s | 639 ms | 697K |
| Coding | C | Runs well | 165.2 tok/s | 1172 ms | 697K |
| Agentic Coding | C | Runs well | 165.2 tok/s | 1704 ms | 697K |
| Reasoning | C | Runs well | 165.2 tok/s | 1385 ms | 697K |
| RAG | C | Runs well | 165.2 tok/s | 2130 ms | 697K |
How internlm2 limarp chat 20b (20B params) fits at each quantization level on Intel Data Center GPU Max 1550 128GB (128.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 7.8 GB | Low | D38 |
Q3_K_S | 3 | 9.8 GB | Low | D38 |
NVFP4 | 4 |
Copy-paste commands to run internlm2 limarp chat 20b on your machine.
Run
lms load hf-intervitens-archive--internlm2-limarp-chat-20b-gguf && lms server startYes, Intel Data Center GPU Max 1550 128GB can run internlm2 limarp chat 20b with a C grade (Runs well). Expected decode speed: 165.2 tok/s.
internlm2 limarp chat 20b (20B parameters) requires approximately 28.2 GB of memory with Q4_K_M quantization.
The recommended quantization for internlm2 limarp chat 20b is Q4_K_M, which balances quality and memory efficiency.
On Intel Data Center GPU Max 1550 128GB, internlm2 limarp chat 20b achieves approximately 165.2 tokens per second decode speed with a time-to-first-token of 1172ms using Q4_K_M quantization.
For coding workloads, internlm2 limarp chat 20b on Intel Data Center GPU Max 1550 128GB receives a C grade with 165.2 tok/s and 697K context.
On Intel Data Center GPU Max 1550 128GB, internlm2 limarp chat 20b can safely use up to 697K 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-intervitens-archive--internlm2-limarp-chat-20b-gguf-on-max-1550-128gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
11.2 GB |
| Medium |
| D38 |
Q4_K_M | 4 | 12.2 GB | Medium | D38 |
Q5_K_M | 5 | 14.4 GB | High | D38 |
Q6_K | 6 | 16.4 GB | High | D38 |
Q8_0 | 8 | 21.4 GB | Very High | D39 |
F16Best for your GPU | 16 | 41.0 GB | Maximum | C42 |
Prefer CUDA if you want the path of least resistance. If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.