Can TinyLlama 1.1B Chat v0.6 run on Radeon RX 7900M 16GB?

YES — Runs Great

C41Usable

Estimated from fit model

TinyLlama 1.1B Chat v0.6 needs ~3.3 GB VRAM. Radeon RX 7900M 16GB has 16.0 GB. With Q4_K_M quantization, expect ~15 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) — 3.3 GB, 15.4 tok/s, Runs well

3.3 GB required16.0 GB available

21% VRAM used

Fit status

Runs well

Decode

15.4 tok/s

TTFT

12571 ms

Safe context

1.6M

Memory

3.3 GB / 16.0 GB

Memory breakdown

Weights0.7 GB

KV Cache0.1 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsTinyLlama 1.1B Chat v0.6 on Radeon RX 7900M 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: 15.4 tok/s decode · 12.6s TTFT (warm) · 39 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	15.4 tok/s	6857 ms	1.0M
Coding	C	Runs well	15.4 tok/s	12571 ms	1.6M
Agentic Coding	C	Runs well	15.4 tok/s	18286 ms	1.6M
Reasoning	C	Runs well	15.4 tok/s	14857 ms	1.6M
RAG	C	Runs well	15.4 tok/s	22857 ms	1.6M

Quantization options

How TinyLlama 1.1B Chat v0.6 (1.100000023841858B params) fits at each quantization level on Radeon RX 7900M 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.4 GB	Low	C45
Q3_K_S	3	0.5 GB	Low	C45
NVFP4	4

Get started

Copy-paste commands to run TinyLlama 1.1B Chat v0.6 on your machine.

Run

lms load hf-tinyllama--tinyllama-1-1b-chat-v0-6 && lms server start

Upgrade options

Hardware that runs TinyLlama 1.1B Chat v0.6 well

MacBook Pro M4 32GBBudget pick

32 GB Unified (+16)

Adds memory headroom for longer context windows and future model growth.15.4 tok/s decode

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

~$799 MSRP

MacBook Pro M3 24GBBest value

24 GB Unified (+8)

This setup is broadly balanced for this model.15.4 tok/s decode

~$1,099 MSRP

Frequently asked questions

Can Radeon RX 7900M 16GB run TinyLlama 1.1B Chat v0.6?

Yes, Radeon RX 7900M 16GB can run TinyLlama 1.1B Chat v0.6 with a C grade (Runs well). Expected decode speed: 15.4 tok/s.

How much VRAM does TinyLlama 1.1B Chat v0.6 need?

TinyLlama 1.1B Chat v0.6 (1.100000023841858B parameters) requires approximately 3.3 GB of memory with Q4_K_M quantization.

What is the best quantization for TinyLlama 1.1B Chat v0.6?

The recommended quantization for TinyLlama 1.1B Chat v0.6 is Q4_K_M, which balances quality and memory efficiency.

What speed will TinyLlama 1.1B Chat v0.6 run at on Radeon RX 7900M 16GB?

On Radeon RX 7900M 16GB, TinyLlama 1.1B Chat v0.6 achieves approximately 15.4 tokens per second decode speed with a time-to-first-token of 12571ms using Q4_K_M quantization.

Can Radeon RX 7900M 16GB run TinyLlama 1.1B Chat v0.6 for coding?

For coding workloads, TinyLlama 1.1B Chat v0.6 on Radeon RX 7900M 16GB receives a C grade with 15.4 tok/s and 1.6M context.

What context window can TinyLlama 1.1B Chat v0.6 use on Radeon RX 7900M 16GB?

On Radeon RX 7900M 16GB, TinyLlama 1.1B Chat v0.6 can safely use up to 1.6M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Radeon RX 7900M 16GB See all hardware for TinyLlama 1.1B Chat v0.6

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