Can DeepSeek Coder V2 16B run on RX 9070 XT 16GB?

YES — With Offload

A82Great

Estimated from fit model

DeepSeek Coder V2 16B needs ~15.6 GB VRAM. RX 9070 XT 16GB has 16.0 GB. With Q4_K_M quantization, expect ~100 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: 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) — 15.6 GB, 99.9 tok/s, Runs with offload

15.6 GB required16.0 GB available

98% VRAM used

Fit status

Runs with offload

Decode

99.9 tok/s

TTFT

1938 ms

Safe context

18K

Memory

15.6 GB / 16.0 GB

Memory breakdown

Weights9.8 GB

KV Cache3.3 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsDeepSeek Coder V2 16B on RX 9070 XT 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: 99.9 tok/s decode · 1.9s TTFT (warm) · 250 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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Tight fit	99.9 tok/s	1057 ms	18K
Coding	A	Runs with offload	99.9 tok/s	1938 ms	18K
Agentic Coding	B	Very compromised (needs ~1.5 GB host RAM)	54.7 tok/s	5150 ms	18K
Reasoning	A	Runs with offload	99.9 tok/s	2290 ms	18K
RAG	B	Very compromised (needs ~1.5 GB host RAM)	54.7 tok/s	6437 ms	18K

Inference speed

DeepSeek Coder V2 16B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for DeepSeek Coder V2 16B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~293 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 / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	292.9	Fits
RTX 4090 24GB	24 GB	Q4_K_M	186.9	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	168.6	Fits
RTX 3090 24GB	24 GB	Q4_K_M	159.8	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	149.0	Offloads
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	135.9	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	113.2	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	107.3	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	83.9	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	83.9	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	58.5	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	53.7	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	51.3	Fits
RTX 4070 12GB	12 GB	Q4_K_M	40.6	Too big
RTX 3060 12GB	12 GB	Q4_K_M	25.5	Too big
RTX 4060 8GB	8 GB	Q4_K_M	9.6	Too big

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 DeepSeek Coder V2 16B (16B params) fits at each quantization level on RX 9070 XT 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	6.2 GB	Low	A79
Q3_K_S	3	7.8 GB	Low	A80
NVFP4	4	9.0 GB	Medium	A80
Q4_K_M	4	9.8 GB	Medium	A80
Q5_K_MBest for your GPU	5	11.5 GB	High	A79
Q6_K	6	13.1 GB	High	F0
Q8_0	8	17.1 GB	Very High	F0
F16	16	32.8 GB	Maximum	F0

Get started

Copy-paste commands to run DeepSeek Coder V2 16B on your machine.

Run

lms load DeepSeek-Coder-V2-Lite-Instruct && lms server start

Your hardware

More models your RX 9070 XT 16GB can run

Model	Params	Grade	Decode
GPT-OSS 20B	21B	A	48.6 tok/s
Codestral 2 25.08	22B	A	17.8 tok/s
CogVLM2 19B	19B	A	27.2 tok/s
Granite Code 20B	20B	B	22.1 tok/s

Frequently asked questions

Can RX 9070 XT 16GB run DeepSeek Coder V2 16B?

Yes, RX 9070 XT 16GB can run DeepSeek Coder V2 16B with a A grade (Runs with offload). Expected decode speed: 99.9 tok/s.

How much VRAM does DeepSeek Coder V2 16B need?

DeepSeek Coder V2 16B (16B parameters) requires approximately 15.6 GB of memory with Q4_K_M quantization.

What is the best quantization for DeepSeek Coder V2 16B?

The recommended quantization for DeepSeek Coder V2 16B is Q4_K_M, which balances quality and memory efficiency.

What speed will DeepSeek Coder V2 16B run at on RX 9070 XT 16GB?

On RX 9070 XT 16GB, DeepSeek Coder V2 16B achieves approximately 99.9 tokens per second decode speed with a time-to-first-token of 1938ms using Q4_K_M quantization.

Can RX 9070 XT 16GB run DeepSeek Coder V2 16B for coding?

For coding workloads, DeepSeek Coder V2 16B on RX 9070 XT 16GB receives a A grade with 99.9 tok/s and 18K context.

What context window can DeepSeek Coder V2 16B use on RX 9070 XT 16GB?

On RX 9070 XT 16GB, DeepSeek Coder V2 16B can safely use up to 18K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if DeepSeek Coder V2 16B feels slow on RX 9070 XT 16GB?

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 9070 XT 16GB See all hardware for DeepSeek Coder V2 16B

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