RX 7900 XTX 24GBGünstige Wahl
24 GB VRAM (+4)960 GB/s (+160)
C
Raises estimated decode speed by about 44%.56.7 tok/s Dekodierung
Raises estimated decode speed by about 44%.
ca. $999 MSRP
Can internlm2 5 20b chat run on RX 7900 XT 20GB?
YES — Tight Fit
C51Usable
Estimated from fit model
internlm2 5 20b chat needs ~17.4 GB VRAM. RX 7900 XT 20GB has 20.0 GB. With Q4_K_M quantization, expect ~39 tok/s.
Runtime: llama.cppCapacity: TightBandwidth: HighStack: 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) — 17.4 GB, 39.3 tok/s, Tight fit
17.4 GB required20.0 GB available
87% VRAM used
Fit status
Tight fit
Decode
39.3 tok/s
TTFT
4921 ms
Safe context
33K
Memory
17.4 GB / 20.0 GB
Memory breakdown
Weights12.2 GB
KV Cache2.3 GB
Runtime0.9 GB
Headroom2.0 GB
See how fast it feels
See how fast it feelsinternlm2 5 20b chat on RX 7900 XT 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: 39.3 tok/s decode · 4.9s TTFT (warm) · 98 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 | 39.3 tok/s | 2684 ms | 33K |
| Coding | C | Tight fit | 39.3 tok/s | 4921 ms | 33K |
| Agentic Coding | C | Runs with offload | 39.3 tok/s | 7157 ms | 33K |
| Reasoning | C | Tight fit | 39.3 tok/s | 5815 ms | 33K |
| RAG | C | Runs with offload | 39.3 tok/s | 8947 ms | 33K |
Quantization options
How internlm2 5 20b chat (20B params) fits at each quantization level on RX 7900 XT 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
AMD Instinct MI60 32GBBestes Preis-Leistungs-Verhältnis
32 GB VRAM (+12)1024 GB/s (+224)
C
Adds memory headroom for longer context windows and future model growth.41.1 tok/s Dekodierung
Adds memory headroom for longer context windows and future model growth.
ca. $8,999 MSRP
AMD Instinct MI100 32GBAMD-Upgrade
32 GB VRAM (+12)1228 GB/s (+428)
C
Raises estimated decode speed by about 66%.65.4 tok/s Dekodierung
Raises estimated decode speed by about 66%.
Adds memory headroom for longer context windows and future model growth.
ca. $11,500 MSRP
Frequently asked questions
Can RX 7900 XT 20GB run internlm2 5 20b chat?
Yes, RX 7900 XT 20GB can run internlm2 5 20b chat with a C grade (Tight fit). Expected decode speed: 39.3 tok/s.
How much VRAM does internlm2 5 20b chat need?
internlm2 5 20b chat (20B parameters) requires approximately 17.4 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 RX 7900 XT 20GB?
On RX 7900 XT 20GB, internlm2 5 20b chat achieves approximately 39.3 tokens per second decode speed with a time-to-first-token of 4921ms using Q4_K_M quantization.
Can RX 7900 XT 20GB run internlm2 5 20b chat for coding?
For coding workloads, internlm2 5 20b chat on RX 7900 XT 20GB receives a C grade with 39.3 tok/s and 33K context.
What context window can internlm2 5 20b chat use on RX 7900 XT 20GB?
On RX 7900 XT 20GB, internlm2 5 20b chat can safely use up to 33K 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-rx-7900-xt-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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