RTX 3090 24GBGünstige Wahl24 GB VRAM (+4)936 GB/s (+576)
C
Raises estimated decode speed by about 133%.53.7 tok/s Dekodierung
Raises estimated decode speed by about 133%.
ca. $1,499 MSRP
Can internlm2 5 20b chat run on RTX 4000 Ada 20GB?
YES — Tight Fit
C49Usable
Estimated from fit model
internlm2 5 20b chat needs ~17.7 GB VRAM. RTX 4000 Ada 20GB has 20.0 GB. With Q4_K_M quantization, expect ~23 tok/s.
Runtime: OllamaCapacity: TightBandwidth: LowStack: BasicBottleneck: 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) — 17.7 GB, 23.0 tok/s, Tight fit
17.7 GB required20.0 GB available
89% VRAM used
Fit status
Tight fit
Decode
23.0 tok/s
TTFT
8411 ms
Safe context
31K
Memory
17.7 GB / 20.0 GB
Memory breakdown
Weights12.2 GB
KV Cache2.3 GB
Runtime1.2 GB
Headroom2.0 GB
See how fast it feels
See how fast it feelsinternlm2 5 20b chat on RTX 4000 Ada 20GB
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: 23.0 tok/s decode · 8.4s TTFT (warm) · 58 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 | Tight fit | 23.0 tok/s | 4588 ms | 31K |
| Coding | C | Tight fit | 23.0 tok/s | 8411 ms | 31K |
| Agentic Coding | C | Runs with offload (needs ~0.1 GB host RAM) | 17.1 tok/s | 16464 ms | 31K |
| Reasoning | C | Tight fit | 23.0 tok/s | 9941 ms | 31K |
| RAG | C | Runs with offload (needs ~0.1 GB host RAM) | 17.1 tok/s | 20580 ms | 31K |
Quantization options
How internlm2 5 20b chat (20B params) fits at each quantization level on RTX 4000 Ada 20GB (20.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 7.8 GB | Low | C49 |
Q3_K_S | 3 | 9.8 GB | Low | C51 |
NVFP4 | 4 | 11.2 GB | Medium | C50 |
Q4_K_M | 4 | 12.2 GB | Medium | C50 |
Q5_K_MBest for your GPU | 5 | 14.4 GB | High | C50 |
Q6_K | 6 | 16.4 GB | High | F0 |
Q8_0 | 8 | 21.4 GB | Very High | F0 |
F16 | 16 | 41.0 GB | Maximum | F0 |
Get started
Copy-paste commands to run internlm2 5 20b chat on your machine.
Run
lms load hf-bartowski--internlm2-5-20b-chat-gguf && lms server startUpgrade-Optionen
Hardware, die internlm2 5 20b chat gut ausführt
RTX 3090 24GBGünstige WahlRTX 4090 24GBBestes Preis-Leistungs-Verhältnis24 GB VRAM (+4)1008 GB/s (+648)
B
Raises estimated decode speed by about 173%.62.8 tok/s Dekodierung
Raises estimated decode speed by about 173%.
ca. $1,599 MSRP
RTX PRO 4000 Blackwell 24GBNVIDIA-Upgrade24 GB VRAM (+4)672 GB/s (+312)
C
Raises estimated decode speed by about 101%.46.3 tok/s Dekodierung
Raises estimated decode speed by about 101%.
ca. $1,599 MSRP
Frequently asked questions
Can RTX 4000 Ada 20GB run internlm2 5 20b chat?
Yes, RTX 4000 Ada 20GB can run internlm2 5 20b chat with a C grade (Tight fit). Expected decode speed: 23.0 tok/s.
How much VRAM does internlm2 5 20b chat need?
internlm2 5 20b chat (20B parameters) requires approximately 17.7 GB of memory with Q4_K_M quantization.
What is the best quantization for internlm2 5 20b chat?
The recommended quantization for internlm2 5 20b chat is Q4_K_M, which balances quality and memory efficiency.
What speed will internlm2 5 20b chat run at on RTX 4000 Ada 20GB?
On RTX 4000 Ada 20GB, internlm2 5 20b chat achieves approximately 23.0 tokens per second decode speed with a time-to-first-token of 8411ms using Q4_K_M quantization.
Can RTX 4000 Ada 20GB run internlm2 5 20b chat for coding?
For coding workloads, internlm2 5 20b chat on RTX 4000 Ada 20GB receives a C grade with 23.0 tok/s and 31K context.
What context window can internlm2 5 20b chat use on RTX 4000 Ada 20GB?
On RTX 4000 Ada 20GB, internlm2 5 20b chat can safely use up to 31K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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<iframe src="https://willitrunai.com/embed/hf-bartowski--internlm2-5-20b-chat-gguf-on-rtx-4000-ada-20gb" 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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