Will It Run AI

Can internlm2 5 1 8b chat i1 run on RX 7600 XT 16GB?

YES — Runs Great

C49Usable
Estimated from fit model

internlm2 5 1 8b chat i1 needs ~8.3 GB VRAM. RX 7600 XT 16GB has 16.0 GB. With Q4_K_M quantization, expect ~34 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: Balanced
Share:

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) — 8.3 GB, 34.2 tok/s, Runs well
8.3 GB required16.0 GB available
52% VRAM used

Fit status

Runs well

Decode

34.2 tok/s

TTFT

5656 ms

Safe context

147K

Memory

8.3 GB / 16.0 GB

Memory breakdown

Weights4.9 GB
KV Cache0.9 GB
Runtime0.9 GB
Headroom1.6 GB

See how fast it feels

See how fast it feelsinternlm2 5 1 8b chat i1 on RX 7600 XT 16GB
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: 34.2 tok/s decode · 5.7s TTFT (warm) · 86 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 well34.2 tok/s3085 ms147K
CodingCRuns well34.2 tok/s5656 ms147K
Agentic CodingCRuns well34.2 tok/s8227 ms147K
ReasoningCRuns well34.2 tok/s6684 ms147K
RAGCRuns well34.2 tok/s10284 ms147K

Quantization options

How internlm2 5 1 8b chat i1 (8B params) fits at each quantization level on RX 7600 XT 16GB (16.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.1 GB
LowC47
Q3_K_S
3
3.9 GB
LowC47
NVFP4
4
4.5 GB
MediumC48
Q4_K_M
4
4.9 GB
MediumC48
Q5_K_M
5
5.8 GB
HighC49
Q6_K
6
6.6 GB
HighC50
Q8_0Best for your GPU
8
8.6 GB
Very HighC51
F16
16
16.4 GB
MaximumF0

Get started

Copy-paste commands to run internlm2 5 1 8b chat i1 on your machine.

Run

lms load hf-mradermacher--internlm2-5-1-8b-chat-i1-gguf && lms server start

Opciones de mejora

Hardware que ejecuta bien internlm2 5 1 8b chat i1

RX 7900 XT 20GBOpción económica
20 GB VRAM (+4)800 GB/s (+512)
C
Sube la velocidad estimada de decodificación alrededor de un 188%.98.4 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 188%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$899 MSRP

RX 7900 XTX 24GBMejor relación calidad-precio
24 GB VRAM (+8)960 GB/s (+672)
C
Sube la velocidad estimada de decodificación alrededor de un 227%.112 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 227%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$999 MSRP

Frequently asked questions

Can RX 7600 XT 16GB run internlm2 5 1 8b chat i1?

Yes, RX 7600 XT 16GB can run internlm2 5 1 8b chat i1 with a C grade (Runs well). Expected decode speed: 34.2 tok/s.

How much VRAM does internlm2 5 1 8b chat i1 need?

internlm2 5 1 8b chat i1 (8B parameters) requires approximately 8.3 GB of memory with Q4_K_M quantization.

What is the best quantization for internlm2 5 1 8b chat i1?

The recommended quantization for internlm2 5 1 8b chat i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will internlm2 5 1 8b chat i1 run at on RX 7600 XT 16GB?

On RX 7600 XT 16GB, internlm2 5 1 8b chat i1 achieves approximately 34.2 tokens per second decode speed with a time-to-first-token of 5656ms using Q4_K_M quantization.

Can RX 7600 XT 16GB run internlm2 5 1 8b chat i1 for coding?

For coding workloads, internlm2 5 1 8b chat i1 on RX 7600 XT 16GB receives a C grade with 34.2 tok/s and 147K context.

What context window can internlm2 5 1 8b chat i1 use on RX 7600 XT 16GB?

On RX 7600 XT 16GB, internlm2 5 1 8b chat i1 can safely use up to 147K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RX 7600 XT 16GBSee all hardware for internlm2 5 1 8b chat i1
Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/hf-mradermacher--internlm2-5-1-8b-chat-i1-gguf-on-rx-7600-xt-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: