Can TinyLlama 1.1B run on RX 6900 XT 16GB?

YES — Runs Great

C52Usable

Estimated from fit model

TinyLlama 1.1B needs ~3.5 GB VRAM. RX 6900 XT 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) — 3.5 GB, 15.4 tok/s, Runs well

3.5 GB required16.0 GB available

22% VRAM used

Fit status

Runs well

Decode

15.4 tok/s

TTFT

12571 ms

Safe context

Memory

3.5 GB / 16.0 GB

Memory breakdown

Weights0.7 GB

KV Cache0.3 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsTinyLlama 1.1B on RX 6900 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: 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	4K
Coding	C	Runs well	15.4 tok/s	12571 ms	4K
Agentic Coding	C	Runs well	15.4 tok/s	18286 ms	4K
Reasoning	C	Runs well	15.4 tok/s	14857 ms	4K
RAG	C	Runs well	15.4 tok/s	22857 ms	4K

Quantization options

How TinyLlama 1.1B (1.100000023841858B params) fits at each quantization level on RX 6900 XT 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.4 GB	Low	B57
Q3_K_S	3	0.5 GB	Low	B57
NVFP4	4	0.6 GB	Medium	B57
Q4_K_M	4	0.7 GB	Medium	B57
Q5_K_M	5	0.8 GB	High	B57
Q6_K	6	0.9 GB	High	B57
Q8_0	8	1.2 GB	Very High	B57
F16Best for your GPU	16	2.3 GB	Maximum	B58

Get started

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

Run

ollama run tinyllama

Opciones de mejora

Hardware que ejecuta bien TinyLlama 1.1B

MacBook Pro M4 32GBOpción económica

32 GB Unified (+16)

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.15.4 tok/s decodificación

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

~$799 MSRP

MacBook Pro M3 24GBMejor relación calidad-precio

24 GB Unified (+8)

Este setup está bastante equilibrado para este modelo.15.4 tok/s decodificación

~$1,099 MSRP

Frequently asked questions

Can RX 6900 XT 16GB run TinyLlama 1.1B?

Yes, RX 6900 XT 16GB can run TinyLlama 1.1B with a C grade (Runs well). Expected decode speed: 15.4 tok/s.

How much VRAM does TinyLlama 1.1B need?

TinyLlama 1.1B (1.100000023841858B parameters) requires approximately 3.5 GB of memory with Q4_K_M quantization.

What is the best quantization for TinyLlama 1.1B?

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

What speed will TinyLlama 1.1B run at on RX 6900 XT 16GB?

On RX 6900 XT 16GB, TinyLlama 1.1B achieves approximately 15.4 tokens per second decode speed with a time-to-first-token of 12571ms using Q4_K_M quantization.

Can RX 6900 XT 16GB run TinyLlama 1.1B for coding?

For coding workloads, TinyLlama 1.1B on RX 6900 XT 16GB receives a C grade with 15.4 tok/s and 4K context.

What context window can TinyLlama 1.1B use on RX 6900 XT 16GB?

On RX 6900 XT 16GB, TinyLlama 1.1B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.

See all results for RX 6900 XT 16GB See all hardware for TinyLlama 1.1B

Embed this result▼

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

<iframe src="https://willitrunai.com/embed/tinyllama-1.1b-on-rx-6900-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: