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URL: https://willitrunai.com/can-run/hf-bartowski--internlm2-5-20b-chat-gguf-on-rx-7900-xtx-24gb

⇱ internlm2 5 20b chat on RX 7900 XTX 24GB? YES

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
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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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well56.7 tok/s1864 ms58K
CodingCRuns well56.7 tok/s3417 ms58K
Agentic CodingCTight fit56.7 tok/s4970 ms58K
ReasoningCRuns well56.7 tok/s4038 ms58K
RAGCTight fit56.7 tok/s6213 ms58K

Quantization options

How internlm2 5 20b chat (20B params) fits at each quantization level on RX 7900 XTX 24GB (24.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
7.8 GB
LowC47
Q3_K_S
3
9.8 GB
LowC48
NVFP4
4
11.2 GB
MediumC49
Q4_K_M
4
12.2 GB
MediumC50
Q5_K_M
5
14.4 GB
HighC50
Q6_KBest for your GPU
6
16.4 GB
HighC49
Q8_0
8
21.4 GB
Very HighF0
F16
16
41.0 GB
MaximumF0

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

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