willitrun·ai

Can TinyLlama 1.1B run on AMD Instinct MI350X 288GB?

YES — Runs Great

C50Usable
Estimated from fit model

TinyLlama 1.1B needs ~30.7 GB VRAM. AMD Instinct MI350X 288GB has 288.0 GB. With Q4_K_M quantization, expect ~15 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: Balanced
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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) — 30.7 GB, 15.4 tok/s, Runs well
30.7 GB required288.0 GB available
11% VRAM used

Fit status

Runs well

Decode

15.4 tok/s

TTFT

12571 ms

Safe context

4K

Memory

30.7 GB / 288.0 GB

Memory breakdown

Weights0.7 GB
KV Cache0.3 GB
Runtime0.9 GB
Headroom28.8 GB

See how fast it feels

See how fast it feelsTinyLlama 1.1B on AMD Instinct MI350X 288GB
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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well15.4 tok/s6857 ms4K
CodingCRuns well15.4 tok/s12571 ms4K
Agentic CodingCRuns well15.4 tok/s18286 ms4K
ReasoningCRuns well15.4 tok/s14857 ms4K
RAGCRuns well15.4 tok/s22857 ms4K

Inference speed

TinyLlama 1.1B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for TinyLlama 1.1B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~21 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / MacMemoryQuantSpeed (tok/s)Fits?
NVIDIARTX 5090 32GB
32 GBQ4_K_M20.9Fits
NVIDIARTX 4090 24GB
24 GBQ4_K_M17.6Fits
NVIDIARTX 4080 Super 16GB
16 GBQ4_K_M17.6Fits
NVIDIARTX 3090 24GB
24 GBQ4_K_M15.4Fits
NVIDIARTX 4070 12GB
12 GBQ4_K_M15.4Fits
NVIDIARTX 3060 12GB
12 GBQ4_K_M15.4Fits
NVIDIARTX 4060 8GB
8 GBQ4_K_M15.4Fits
RX 7900 XTX 24GB
24 GBQ4_K_M15.4Fits
MacBook Pro M4 Max 128GB
128 GBQ4_K_M15.4Fits
Mac Studio M3 Ultra 256GB
256 GBQ4_K_M15.4Fits
Mac Studio M2 Ultra 128GB
128 GBQ4_K_M15.4Fits
Mac Studio M1 Ultra 128GB
128 GBQ4_K_M15.4Fits
MacBook Pro M4 Max 64GB
64 GBQ4_K_M15.4Fits
MacBook Pro M3 Max 64GB
64 GBQ4_K_M15.4Fits
MacBook Pro M1 Max 64GB
64 GBQ4_K_M15.4Fits
MacBook Pro M4 Pro 48GB
48 GBQ4_K_M15.4Fits

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How TinyLlama 1.1B (1.100000023841858B params) fits at each quantization level on AMD Instinct MI350X 288GB (288.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
0.4 GB
LowC47
Q3_K_S
3
0.5 GB
LowC47
NVFP4
4
0.6 GB
MediumC47
Q4_K_M
4
0.7 GB
MediumC47
Q5_K_M
5
0.8 GB
HighC47
Q6_K
6
0.9 GB
HighC47
Q8_0
8
1.2 GB
Very HighC47
F16Best for your GPU
16
2.3 GB
MaximumC47

Get started

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

Run

ollama run tinyllama

Frequently asked questions

Can AMD Instinct MI350X 288GB run TinyLlama 1.1B?

Yes, AMD Instinct MI350X 288GB 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 30.7 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 AMD Instinct MI350X 288GB?

On AMD Instinct MI350X 288GB, 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 AMD Instinct MI350X 288GB run TinyLlama 1.1B for coding?

For coding workloads, TinyLlama 1.1B on AMD Instinct MI350X 288GB receives a C grade with 15.4 tok/s and 4K context.

What context window can TinyLlama 1.1B use on AMD Instinct MI350X 288GB?

On AMD Instinct MI350X 288GB, 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 AMD Instinct MI350X 288GBSee all hardware for TinyLlama 1.1B
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