InternLM 7B needs ~14.9 GB VRAM. RTX 2000 Ada 16GB has 16.0 GB. With Q4_K_M quantization, expect ~51 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
Tight fit
Decode
51.3 tok/s
TTFT
3777 ms
Safe context
8K
Memory
14.9 GB / 16.0 GB
This setup is broadly balanced for this model.
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.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs well | 51.3 tok/s | 2060 ms | 8K |
| Coding | A | Tight fit | 51.3 tok/s | 3777 ms | 8K |
| Agentic Coding | F | Too heavy | 18.4 tok/s | 15288 ms | 8K |
| Reasoning | A | Tight fit | 51.3 tok/s | 4464 ms | 8K |
| RAG | F | Too heavy | 18.4 tok/s | 19110 ms | 8K |
How InternLM 7B (7B params) fits at each quantization level on RTX 2000 Ada 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | B67 |
Q3_K_S | 3 | 3.4 GB | Low | B68 |
NVFP4 | 4 |
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 99Your hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen 3.5 9B | 9B | S | 42.9 tok/s | |
| Qwen 3 14B | 14B | S | 27.7 tok/s |
Yes, RTX 2000 Ada 16GB can run InternLM 7B with a A grade (Tight fit). Expected decode speed: 51.3 tok/s.
InternLM 7B (7B parameters) requires approximately 14.9 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 2000 Ada 16GB, InternLM 7B achieves approximately 51.3 tokens per second decode speed with a time-to-first-token of 3777ms using Q4_K_M quantization.
For coding workloads, InternLM 7B on RTX 2000 Ada 16GB receives a A grade with 51.3 tok/s and 8K context.
On RTX 2000 Ada 16GB, 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/internlm-7b-on-rtx-2000-ada-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
3.9 GB |
| Medium |
| B68 |
Q4_K_M | 4 | 4.3 GB | Medium | B69 |
Q5_K_M | 5 | 5.0 GB | High | B69 |
Q6_K | 6 | 5.7 GB | High | A70 |
Q8_0Best for your GPU | 8 | 7.5 GB | Very High | A72 |
F16 | 16 | 14.3 GB | Maximum | F0 |
| Qwen 3 8B | 8B | S | 48.2 tok/s |
 Phi-4-reasoning-plus 14B | 14.7B | S | 26.2 tok/s |
 GPT-OSS 20B | 21B | A | 24.4 tok/s |
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.