Can internlm2 5 20b chat run on RX 7900 XTX 24GB?

YES — Runs Great

C55Usable

Estimated from fit model

internlm2 5 20b chat needs ~17.8 GB VRAM. RX 7900 XTX 24GB has 24.0 GB. With Q4_K_M quantization, expect ~57 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 17.8 GB, 56.7 tok/s, Runs well

17.8 GB required24.0 GB available

74% VRAM used

Fit status

Runs well

Decode

56.7 tok/s

TTFT

3417 ms

Safe context

58K

Memory

17.8 GB / 24.0 GB

Memory breakdown

Weights12.2 GB

KV Cache2.3 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsinternlm2 5 20b chat on RX 7900 XTX 24GB
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: 56.7 tok/s decode · 3.4s TTFT (warm) · 142 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	56.7 tok/s	1864 ms	58K
Coding	C	Runs well	56.7 tok/s	3417 ms	58K
Agentic Coding	C	Tight fit	56.7 tok/s	4970 ms	58K
Reasoning	C	Runs well	56.7 tok/s	4038 ms	58K
RAG	C	Tight fit	56.7 tok/s	6213 ms	58K

Quantization options

How internlm2 5 20b chat (20B params) fits at each quantization level on RX 7900 XTX 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

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 start

Frequently asked questions

Can RX 7900 XTX 24GB run internlm2 5 20b chat?

Yes, RX 7900 XTX 24GB can run internlm2 5 20b chat with a C grade (Runs well). Expected decode speed: 56.7 tok/s.

How much VRAM does internlm2 5 20b chat need?

internlm2 5 20b chat (20B parameters) requires approximately 17.8 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 XTX 24GB?

On RX 7900 XTX 24GB, internlm2 5 20b chat achieves approximately 56.7 tokens per second decode speed with a time-to-first-token of 3417ms using Q4_K_M quantization.

Can RX 7900 XTX 24GB run internlm2 5 20b chat for coding?

For coding workloads, internlm2 5 20b chat on RX 7900 XTX 24GB receives a C grade with 56.7 tok/s and 58K context.

What context window can internlm2 5 20b chat use on RX 7900 XTX 24GB?

On RX 7900 XTX 24GB, internlm2 5 20b chat can safely use up to 58K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RX 7900 XTX 24GB See all hardware for internlm2 5 20b chat

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<iframe src="https://willitrunai.com/embed/hf-bartowski--internlm2-5-20b-chat-gguf-on-rx-7900-xtx-24gb" 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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