Can internlm2 5 7b chat i1 run on RX 9070 XT 16GB?

YES — Runs Great

C52Usable

Estimated from fit model

internlm2 5 7b chat i1 needs ~7.6 GB VRAM. RX 9070 XT 16GB has 16.0 GB. With Q4_K_M quantization, expect ~96 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 7.6 GB, 95.9 tok/s, Runs well

7.6 GB required16.0 GB available

48% VRAM used

Fit status

Runs well

Decode

95.9 tok/s

TTFT

2018 ms

Safe context

180K

Memory

7.6 GB / 16.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsinternlm2 5 7b chat i1 on RX 9070 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: 95.9 tok/s decode · 2.0s TTFT (warm) · 240 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	95.9 tok/s	1101 ms	180K
Coding	C	Runs well	95.9 tok/s	2018 ms	180K
Agentic Coding	C	Runs well	95.9 tok/s	2936 ms	180K
Reasoning	C	Runs well	95.9 tok/s	2385 ms	180K
RAG	C	Runs well	95.9 tok/s	3670 ms	180K

Quantization options

How internlm2 5 7b chat i1 (7B params) fits at each quantization level on RX 9070 XT 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C46
Q3_K_S	3	3.4 GB	Low	C47
NVFP4	4

Get started

Copy-paste commands to run internlm2 5 7b chat i1 on your machine.

Run

lms load hf-mradermacher--internlm2-5-7b-chat-i1-gguf && lms server start

Frequently asked questions

Can RX 9070 XT 16GB run internlm2 5 7b chat i1?

Yes, RX 9070 XT 16GB can run internlm2 5 7b chat i1 with a C grade (Runs well). Expected decode speed: 95.9 tok/s.

How much VRAM does internlm2 5 7b chat i1 need?

internlm2 5 7b chat i1 (7B parameters) requires approximately 7.6 GB of memory with Q4_K_M quantization.

What is the best quantization for internlm2 5 7b chat i1?

The recommended quantization for internlm2 5 7b chat i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will internlm2 5 7b chat i1 run at on RX 9070 XT 16GB?

On RX 9070 XT 16GB, internlm2 5 7b chat i1 achieves approximately 95.9 tokens per second decode speed with a time-to-first-token of 2018ms using Q4_K_M quantization.

Can RX 9070 XT 16GB run internlm2 5 7b chat i1 for coding?

For coding workloads, internlm2 5 7b chat i1 on RX 9070 XT 16GB receives a C grade with 95.9 tok/s and 180K context.

What context window can internlm2 5 7b chat i1 use on RX 9070 XT 16GB?

On RX 9070 XT 16GB, internlm2 5 7b chat i1 can safely use up to 180K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RX 9070 XT 16GB See all hardware for internlm2 5 7b chat i1

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