Will It Run AI

Can Yi Coder 9B Chat run on RX 5700 XT 8GB?

YES — With Offload

C50Usable
Estimated from fit model

Yi Coder 9B Chat needs ~8.2 GB VRAM. RX 5700 XT 8GB has 8.0 GB. With Q4_K_M quantization, expect ~30 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: 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) 8.2 GB, 29.9 tok/s, Runs with offload (needs ~0.2 GB host RAM)
8.2 GB required8.0 GB available
102% VRAM needed

0.2 GB over capacity — needs offload or smaller quantization

Fit status

Runs with offload (needs ~0.2 GB host RAM)

Decode

29.9 tok/s

TTFT

6482 ms

Safe context

12K

Memory

8.2 GB / 8.0 GB

Memory breakdown

Weights5.5 GB
KV Cache1.1 GB
Runtime0.9 GB
Headroom0.8 GB

See how fast it feels

See how fast it feelsYi Coder 9B Chat on RX 5700 XT 8GB
1st promptCold start — includes initialization
>Write a Python function to sort a list
Here's a Python function that sorts a list using quicksort: def quicksort(arr): if len(arr) <= 1: return arr pivot = arr[len(arr) // 2] left = [x for x in arr if x < pivot] middle = [x for x in arr if x == pivot] right = [x for x in arr if x > pivot] return quicksort(left) + middle + quicksort(right) # Example usage numbers = [3, 6, 8, 10, 1, 2, 1] print(quicksort(numbers)) # [1, 1, 2, 3, 6, 8, 10] The algorithm picks a pivot, partitions the list into smaller, equal, and larger elements, then recursively sorts each partition and concatenates the results.
2nd promptWarm — model ready, no init overhead
>Now add type hints and a docstring
def quicksort(arr: list[int]) -> list[int]: """Sort a list using the quicksort algorithm. Args: arr: List of integers to sort. Returns: A new sorted list. """ if len(arr) <= 1: return arr pivot = arr[len(arr) // 2] left = [x for x in arr if x < pivot] middle = [x for x in arr if x == pivot] right = [x for x in arr if x > pivot] return quicksort(left) + middle + quicksort(right) Added type hints for the input and return type, plus a Google-style docstring.
Estimated: 29.9 tok/s decode · 6.5s TTFT (warm) · 75 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns with offload42.4 tok/s2489 ms12K
CodingCRuns with offload (needs ~0.2 GB host RAM)29.9 tok/s6482 ms12K
Agentic CodingDVery compromised (needs ~0.8 GB host RAM)23.2 tok/s12147 ms12K
ReasoningCRuns with offload (needs ~0.2 GB host RAM)29.9 tok/s7660 ms12K
RAGDVery compromised (needs ~0.8 GB host RAM)23.2 tok/s15183 ms12K

Quantization options

How Yi Coder 9B Chat (9B params) fits at each quantization level on RX 5700 XT 8GB (8.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.5 GB
LowC54
Q3_K_S
3
4.4 GB
LowC53
NVFP4Best for your GPU
4
5.0 GB
MediumC53
Q4_K_M
4
5.5 GB
MediumF0
Q5_K_M
5
6.5 GB
HighF0
Q6_K
6
7.4 GB
HighF0
Q8_0
8
9.6 GB
Very HighF0
F16
16
18.5 GB
MaximumF0

Get started

Copy-paste commands to run Yi Coder 9B Chat on your machine.

Run

lms load hf-maziyarpanahi--yi-coder-9b-chat-gguf && lms server start

Opciones de mejora

Hardware que ejecuta bien Yi Coder 9B Chat

Frequently asked questions

Can RX 5700 XT 8GB run Yi Coder 9B Chat?

Yes, RX 5700 XT 8GB can run Yi Coder 9B Chat with a C grade (Runs with offload (needs ~0.2 GB host RAM)). Expected decode speed: 29.9 tok/s.

How much VRAM does Yi Coder 9B Chat need?

Yi Coder 9B Chat (9B parameters) requires approximately 8.2 GB of memory with Q4_K_M quantization.

What is the best quantization for Yi Coder 9B Chat?

The recommended quantization for Yi Coder 9B Chat is Q4_K_M, which balances quality and memory efficiency.

What speed will Yi Coder 9B Chat run at on RX 5700 XT 8GB?

On RX 5700 XT 8GB, Yi Coder 9B Chat achieves approximately 29.9 tokens per second decode speed with a time-to-first-token of 6482ms using Q4_K_M quantization.

Can RX 5700 XT 8GB run Yi Coder 9B Chat for coding?

For coding workloads, Yi Coder 9B Chat on RX 5700 XT 8GB receives a C grade with 29.9 tok/s and 12K context.

What context window can Yi Coder 9B Chat use on RX 5700 XT 8GB?

On RX 5700 XT 8GB, Yi Coder 9B Chat can safely use up to 12K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if Yi Coder 9B Chat feels slow on RX 5700 XT 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 5700 XT 8GBSee all hardware for Yi Coder 9B Chat
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