InternLM 7B needs ~14.2 GB VRAM. RTX A2000 12GB has 12.0 GB. With Q4_K_M quantization, expect ~28 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
2.2 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~0.7 GB host RAM)
Decode
27.8 tok/s
TTFT
6975 ms
Safe context
8K
Memory
14.2 GB / 12.0 GB
Offload
20%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 20% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly 0.7 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Tight fit | 52.6 tok/s | 2007 ms | 8K |
| Coding | C | Very compromised (needs ~0.7 GB host RAM) | 27.8 tok/s | 6975 ms | 8K |
| Agentic Coding | F | Too heavy | 11.0 tok/s | 25552 ms | 8K |
| Reasoning | C | Very compromised (needs ~0.7 GB host RAM) | 27.8 tok/s | 8243 ms | 8K |
| RAG | F | Too heavy | 11.0 tok/s | 31940 ms | 8K |
How InternLM 7B (7B params) fits at each quantization level on RTX A2000 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | B70 |
Q3_K_S | 3 | 3.4 GB | Low | A70 |
NVFP4 | 4 | 3.9 GB | Medium | A71 |
Q4_K_M | 4 | 4.3 GB | Medium | A72 |
Q5_K_M | 5 | 5.0 GB | High | A73 |
Q6_K | 6 | 5.7 GB | High | A73 |
Q8_0Best for your GPU | 8 | 7.5 GB | Very High | A72 |
F16 | 16 | 14.3 GB | Maximum | F0 |
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 99Opciones de mejora
RTX 5060 Ti 16GBOpción económicaElimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
Sube la velocidad estimada de decodificación alrededor de un 134%.
~$449 MSRP
RTX 4060 Ti 16GBMejor relación calidad-precioElimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
Sube la velocidad estimada de decodificación alrededor de un 63%.
~$499 MSRP
RTX 2000 Ada 16GBMejora NVIDIAElimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
Sube la velocidad estimada de decodificación alrededor de un 85%.
~$625 MSRP
Yes, RTX A2000 12GB can run InternLM 7B with a C grade (Very compromised (needs ~0.7 GB host RAM)). Expected decode speed: 27.8 tok/s.
InternLM 7B (7B parameters) requires approximately 14.2 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 RTX A2000 12GB, InternLM 7B achieves approximately 27.8 tokens per second decode speed with a time-to-first-token of 6975ms using Q4_K_M quantization.
For coding workloads, InternLM 7B on RTX A2000 12GB receives a C grade with 27.8 tok/s and 8K context.
On RTX A2000 12GB, 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.
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/internlm-7b-on-a2000-12gb" 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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