Can internlm2 5 1 8b chat i1 run on RX 9060 8GB?

YES — Tight Fit

C50Usable

Estimated from fit model

internlm2 5 1 8b chat i1 needs ~7.5 GB VRAM. RX 9060 8GB has 8.0 GB. With Q4_K_M quantization, expect ~37 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: LowStack: 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) — 7.5 GB, 37.2 tok/s, Tight fit

7.5 GB required8.0 GB available

94% VRAM used

Fit status

Tight fit

Decode

37.2 tok/s

TTFT

5207 ms

Safe context

24K

Memory

7.5 GB / 8.0 GB

Memory breakdown

Weights4.9 GB

KV Cache0.9 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsinternlm2 5 1 8b chat i1 on RX 9060 8GB
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: 37.2 tok/s decode · 5.2s TTFT (warm) · 93 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Tight fit	37.2 tok/s	2840 ms	24K
Coding	C	Tight fit	37.2 tok/s	5207 ms	24K
Agentic Coding	D	Runs with offload	24.8 tok/s	11346 ms	24K
Reasoning	C	Tight fit	37.2 tok/s	6154 ms	24K
RAG	D	Runs with offload	24.8 tok/s	14182 ms	24K

Quantization options

How internlm2 5 1 8b chat i1 (8B params) fits at each quantization level on RX 9060 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C53
Q3_K_S	3	3.9 GB	Low	C53
NVFP4	4	4.5 GB	Medium	C53
Q4_K_MBest for your GPU	4	4.9 GB	Medium	C52
Q5_K_M	5	5.8 GB	High	F0
Q6_K	6	6.6 GB	High	F0
Q8_0	8	8.6 GB	Very High	F0
F16	16	16.4 GB	Maximum	F0

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

Opções de upgrade

Hardware que roda bem internlm2 5 1 8b chat i1

RX 7700 XT 12GBOpção econômica

12 GB VRAM (+4)432 GB/s (+144)

Raises estimated decode speed by about 43%.53.1 tok/s decodificação

Raises estimated decode speed by about 43%.

Adds memory headroom for longer context windows and future model growth.

~$449 MSRP

RX 6700 XT 12GBMelhor custo-benefício

12 GB VRAM (+4)384 GB/s (+96)

Adds memory headroom for longer context windows and future model growth.40.9 tok/s decodificação

Adds memory headroom for longer context windows and future model growth.

~$479 MSRP

RX 9070 16GBUpgrade AMD

16 GB VRAM (+8)640 GB/s (+352)

Raises estimated decode speed by about 119%.81.3 tok/s decodificação

Raises estimated decode speed by about 119%.

Adds memory headroom for longer context windows and future model growth.

~$479 MSRP

Frequently asked questions

Can RX 9060 8GB run internlm2 5 1 8b chat i1?

Yes, RX 9060 8GB can run internlm2 5 1 8b chat i1 with a C grade (Tight fit). Expected decode speed: 37.2 tok/s.

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

internlm2 5 1 8b chat i1 (8B parameters) requires approximately 7.5 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 9060 8GB?

On RX 9060 8GB, internlm2 5 1 8b chat i1 achieves approximately 37.2 tokens per second decode speed with a time-to-first-token of 5207ms using Q4_K_M quantization.

Can RX 9060 8GB run internlm2 5 1 8b chat i1 for coding?

For coding workloads, internlm2 5 1 8b chat i1 on RX 9060 8GB receives a C grade with 37.2 tok/s and 24K context.

What context window can internlm2 5 1 8b chat i1 use on RX 9060 8GB?

On RX 9060 8GB, internlm2 5 1 8b chat i1 can safely use up to 24K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if internlm2 5 1 8b chat i1 feels slow on RX 9060 8GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

See all results for RX 9060 8GB See 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-9060-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: