Can StarCoder2 15B run on RX 9070 16GB?

YES — Tight Fit

C53Usable

Estimated from fit model

StarCoder2 15B needs ~14.5 GB VRAM. RX 9070 16GB has 16.0 GB. With Q5_K_M quantization, expect ~41 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: 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

Q5_K_M (High quality) — 14.5 GB, 40.9 tok/s, Tight fit

14.5 GB required16.0 GB available

91% VRAM used

Fit status

Tight fit

Decode

40.9 tok/s

TTFT

4732 ms

Safe context

16K

Memory

14.5 GB / 16.0 GB

Memory breakdown

Weights10.8 GB

KV Cache1.2 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsStarCoder2 15B on RX 9070 16GB
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: 40.9 tok/s decode · 4.7s TTFT (warm) · 102 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	Tight fit	40.9 tok/s	2581 ms	16K
Coding	C	Tight fit	40.9 tok/s	4732 ms	16K
Agentic Coding	C	Runs with offload	40.9 tok/s	6883 ms	16K
Reasoning	C	Tight fit	40.9 tok/s	5593 ms	16K
RAG	C	Runs with offload	40.9 tok/s	8604 ms	16K

Quantization options

How StarCoder2 15B (15B params) fits at each quantization level on RX 9070 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	C51
Q3_K_S	3	7.4 GB	Low	C53
NVFP4	4	8.4 GB	Medium	C53
Q4_K_M	4	9.2 GB	Medium	C53
Q5_K_M	5	10.8 GB	High	C52
Q6_KBest for your GPU	6	12.3 GB	High	C52
Q8_0	8	16.1 GB	Very High	F0
F16	16	30.7 GB	Maximum	F0

Get started

Copy-paste commands to run StarCoder2 15B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "bigcode/starcoder2-15b" \
  --hf-file "starcoder2-15b-Q5_K_M.gguf" \
  -c 4096 -ngl 99

アップグレードオプション

StarCoder2 15Bを快適に動かすハードウェア

RX 7900 XT 20GBコスパ重視

20 GB VRAM (+4)800 GB/s (+160)

Adds memory headroom for longer context windows and future model growth.49.5 tok/sデコード

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

〜$899 MSRP

RX 7900 XTX 24GBコスパ最良

24 GB VRAM (+8)960 GB/s (+320)

Raises estimated decode speed by about 74%.71.3 tok/sデコード

Raises estimated decode speed by about 74%.

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

〜$999 MSRP

AMD Instinct MI100 32GBAMDアップグレード

32 GB VRAM (+16)1228 GB/s (+588)

Raises estimated decode speed by about 101%.82.3 tok/sデコード

Raises estimated decode speed by about 101%.

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

〜$11,500 MSRP

Frequently asked questions

Can RX 9070 16GB run StarCoder2 15B?

Yes, RX 9070 16GB can run StarCoder2 15B with a C grade (Tight fit). Expected decode speed: 40.9 tok/s.

How much VRAM does StarCoder2 15B need?

StarCoder2 15B (15B parameters) requires approximately 14.5 GB of memory with Q5_K_M quantization.

What is the best quantization for StarCoder2 15B?

The recommended quantization for StarCoder2 15B is Q5_K_M, which balances quality and memory efficiency.

What speed will StarCoder2 15B run at on RX 9070 16GB?

On RX 9070 16GB, StarCoder2 15B achieves approximately 40.9 tokens per second decode speed with a time-to-first-token of 4732ms using Q5_K_M quantization.

Can RX 9070 16GB run StarCoder2 15B for coding?

For coding workloads, StarCoder2 15B on RX 9070 16GB receives a C grade with 40.9 tok/s and 16K context.

What context window can StarCoder2 15B use on RX 9070 16GB?

On RX 9070 16GB, StarCoder2 15B can safely use up to 16K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.

See all results for RX 9070 16GB See all hardware for StarCoder2 15B

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