MacBook Pro M4 Max 48GBOpción económica
48 GB Unified (+16)
C
Este setup está bastante equilibrado para este modelo.35.6 tok/s decodificación
~$2,499 MSRP
Can internlm2 limarp chat 20b run on Radeon AI PRO R9700 32GB?
YES — Runs Great
C50Usable
Estimated from fit model
internlm2 limarp chat 20b needs ~18.6 GB VRAM. Radeon AI PRO R9700 32GB has 32.0 GB. With Q4_K_M quantization, expect ~31 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: Balanced
Operating mode
Choose the run profile you care about
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
Q4_K_M (Medium quality) — 18.6 GB, 31.0 tok/s, Runs well
18.6 GB required32.0 GB available
58% VRAM used
Fit status
Runs well
Decode
31.0 tok/s
TTFT
6255 ms
Safe context
107K
Memory
18.6 GB / 32.0 GB
Memory breakdown
Weights12.2 GB
KV Cache2.3 GB
Runtime0.9 GB
Headroom3.2 GB
See how fast it feels
See how fast it feelsinternlm2 limarp chat 20b on Radeon AI PRO R9700 32GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 31.0 tok/s decode · 6.3s TTFT (warm) · 77 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 31.0 tok/s | 3412 ms | 107K |
| Coding | C | Runs well | 31.0 tok/s | 6255 ms | 107K |
| Agentic Coding | C | Runs well | 31.0 tok/s | 9098 ms | 107K |
| Reasoning | C | Runs well | 31.0 tok/s | 7392 ms | 107K |
| RAG | C | Runs well | 31.0 tok/s | 11373 ms | 107K |
Quantization options
How internlm2 limarp chat 20b (20B params) fits at each quantization level on Radeon AI PRO R9700 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 7.8 GB | Low | C45 |
Q3_K_S | 3 | 9.8 GB | Low | C45 |
NVFP4 | 4 | 11.2 GB | Medium | C46 |
Q4_K_M | 4 | 12.2 GB | Medium | C47 |
Q5_K_M | 5 | 14.4 GB | High | C48 |
Q6_K | 6 | 16.4 GB | High | C49 |
Q8_0Best for your GPU | 8 | 21.4 GB | Very High | C49 |
F16 | 16 | 41.0 GB | Maximum | F0 |
Get started
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 startOpciones de mejora
Hardware que ejecuta bien internlm2 limarp chat 20b
RTX PRO 5000 Blackwell 48GBMejor relación calidad-precio48 GB VRAM (+16)1344 GB/s (+704)
C
Sube la velocidad estimada de decodificación alrededor de un 198%.92.5 tok/s decodificación
Sube la velocidad estimada de decodificación alrededor de un 198%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$4,999 MSRP
Frequently asked questions
Can Radeon AI PRO R9700 32GB run internlm2 limarp chat 20b?
Yes, Radeon AI PRO R9700 32GB can run internlm2 limarp chat 20b with a C grade (Runs well). Expected decode speed: 31.0 tok/s.
How much VRAM does internlm2 limarp chat 20b need?
internlm2 limarp chat 20b (20B parameters) requires approximately 18.6 GB of memory with Q4_K_M quantization.
What is the best quantization for internlm2 limarp chat 20b?
The recommended quantization for internlm2 limarp chat 20b is Q4_K_M, which balances quality and memory efficiency.
What speed will internlm2 limarp chat 20b run at on Radeon AI PRO R9700 32GB?
On Radeon AI PRO R9700 32GB, internlm2 limarp chat 20b achieves approximately 31.0 tokens per second decode speed with a time-to-first-token of 6255ms using Q4_K_M quantization.
Can Radeon AI PRO R9700 32GB run internlm2 limarp chat 20b for coding?
For coding workloads, internlm2 limarp chat 20b on Radeon AI PRO R9700 32GB receives a C grade with 31.0 tok/s and 107K context.
What context window can internlm2 limarp chat 20b use on Radeon AI PRO R9700 32GB?
On Radeon AI PRO R9700 32GB, internlm2 limarp chat 20b can safely use up to 107K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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