internlm2 5 20b chat needs ~18.1 GB VRAM. RTX 3090 Ti 24GB has 24.0 GB. With Q4_K_M quantization, expect ~59 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
58.7 tok/s
TTFT
3300 ms
Safe context
56K
Memory
18.1 GB / 24.0 GB
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 58.7 tok/s | 1800 ms | 56K |
| Coding | B | Runs well | 58.7 tok/s | 3300 ms | 56K |
| Agentic Coding | C | Tight fit | 58.7 tok/s | 4800 ms | 56K |
| Reasoning | B | Runs well | 58.7 tok/s | 3900 ms | 56K |
| RAG | C | Tight fit | 58.7 tok/s | 6000 ms | 56K |
How internlm2 5 20b chat (20B params) fits at each quantization level on RTX 3090 Ti 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 7.8 GB | Low | C47 |
Q3_K_S | 3 | 9.8 GB | Low | C48 |
NVFP4 | 4 | 11.2 GB | Medium | C49 |
Q4_K_M | 4 | 12.2 GB | Medium | C50 |
Q5_K_M | 5 | 14.4 GB | High | C50 |
Q6_KBest for your GPU | 6 | 16.4 GB | High | C49 |
Q8_0 | 8 | 21.4 GB | Very High | F0 |
F16 | 16 | 41.0 GB | Maximum | F0 |
Copy-paste commands to run internlm2 5 20b chat on your machine.
Run
lms load hf-bartowski--internlm2-5-20b-chat-gguf && lms server startYes, RTX 3090 Ti 24GB can run internlm2 5 20b chat with a B grade (Runs well). Expected decode speed: 58.7 tok/s.
internlm2 5 20b chat (20B parameters) requires approximately 18.1 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 RTX 3090 Ti 24GB, internlm2 5 20b chat achieves approximately 58.7 tokens per second decode speed with a time-to-first-token of 3300ms using Q4_K_M quantization.
For coding workloads, internlm2 5 20b chat on RTX 3090 Ti 24GB receives a B grade with 58.7 tok/s and 56K context.
On RTX 3090 Ti 24GB, internlm2 5 20b chat can safely use up to 56K 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-bartowski--internlm2-5-20b-chat-gguf-on-rtx-3090-ti-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: