Can DeepSeek Coder V2 236B run on RTX A5000 24GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

DeepSeek Coder V2 236B needs ~205.9 GB but RTX A5000 24GB only has 24.0 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: MediumStack: StandardBottleneck: Memory capacity

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) — 205.9 GB, exceeds 24.0 GB available

205.9 GB required24.0 GB available

858% VRAM needed

181.9 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.0 tok/s

TTFT

96800 ms

Safe context

Memory

205.9 GB / 24.0 GB

Offload

90%

Memory breakdown

Weights144.0 GB

KV Cache58.6 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsDeepSeek Coder V2 236B on RTX A5000 24GB
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: 2.0 tok/s decode · 96.8s TTFT (warm) · 5 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 205.9 GB, but this setup only exposes 24.0 GB of usable VRAM.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	2.0 tok/s	52800 ms	4K
Coding	F	Too heavy	2.0 tok/s	96800 ms	4K
Agentic Coding	F	Too heavy	2.0 tok/s	140800 ms	4K
Reasoning	F	Too heavy	2.0 tok/s	114400 ms	4K
RAG	F	Too heavy	2.0 tok/s	176000 ms	4K

Inference speed

DeepSeek Coder V2 236B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for DeepSeek Coder V2 236B at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is Mac Studio M3 Ultra 256GB at ~12 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?
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	11.7	Too big
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	6.1	Too big
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	5.7	Too big
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	4.5	Too big
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	4.5	Too big
RTX 5090 32GB	32 GB	Q4_K_M	3.4	Too big
2× RX 7900 XTX 24GB	48 GB	Q4_K_M	3.3	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	3.1	Too big
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	2.9	Too big
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	2.7	Too big
2× RTX 4090 24GB	48 GB	Q4_K_M	2.3	Too big
RTX 4090 24GB	24 GB	Q4_K_M	2.2	Too big
RTX 4080 Super 16GB	16 GB	Q4_K_M	2.0	Too big
RTX 3090 24GB	24 GB	Q4_K_M	2.0	Too big
RTX 4070 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 3060 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.0	Too big
RX 7900 XTX 24GB	24 GB	Q4_K_M	2.0	Too big
2× RTX 3090 24GB	48 GB	Q4_K_M	2.0	Too big
4× RTX 3060 12GB	48 GB	Q4_K_M	2.0	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 236B (236B params) fits at each quantization level on RTX A5000 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	92.0 GB	Low	F0
Q3_K_S	3	115.6 GB	Low	F0
NVFP4	4	132.2 GB	Medium	F0
Q4_K_M	4	144.0 GB	Medium	F0
Q5_K_M	5	169.9 GB	High	F0
Q6_K	6	193.5 GB	High	F0
Q8_0	8	252.5 GB	Very High	F0
F16	16	483.8 GB	Maximum	F0

Upgrade options

Hardware that runs DeepSeek Coder V2 236B well

AMD Instinct MI350X 288GBBudget pick

288 GB VRAM (+264)8000 GB/s (+7232)

Makes the model fit on the accelerator instead of staying completely out of reach.109.3 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$8,000 MSRP

B100 192GBBest value

192 GB VRAM (+168)8000 GB/s (+7232)

Makes the model fit on the accelerator instead of staying completely out of reach.84 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 4100%.

~$35,000 MSRP

NVIDIA GB200 192GBNVIDIA upgrade

192 GB VRAM (+168)8000 GB/s (+7232)

Makes the model fit on the accelerator instead of staying completely out of reach.84 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 4100%.

~$60,000 MSRP

Frequently asked questions

Can RTX A5000 24GB run DeepSeek Coder V2 236B?

No, DeepSeek Coder V2 236B requires more memory than RTX A5000 24GB provides.

How much VRAM does DeepSeek Coder V2 236B need?

DeepSeek Coder V2 236B (236B parameters) requires approximately 205.9 GB of memory with Q4_K_M quantization.

What is the best quantization for DeepSeek Coder V2 236B?

The recommended quantization for DeepSeek Coder V2 236B is Q4_K_M, which balances quality and memory efficiency.

What speed will DeepSeek Coder V2 236B run at on RTX A5000 24GB?

On RTX A5000 24GB, DeepSeek Coder V2 236B achieves approximately 2.0 tokens per second decode speed with a time-to-first-token of 96800ms using Q4_K_M quantization.

Can RTX A5000 24GB run DeepSeek Coder V2 236B for coding?

For coding workloads, DeepSeek Coder V2 236B on RTX A5000 24GB receives a F grade with 2.0 tok/s and 4K context.

What context window can DeepSeek Coder V2 236B use on RTX A5000 24GB?

On RTX A5000 24GB, DeepSeek Coder V2 236B can safely use up to 4K 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 236B feels slow on RTX A5000 24GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

See all results for RTX A5000 24GB See all hardware for DeepSeek Coder V2 236B

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Too heavy

2.0 tok/s

52800 ms

Coding

Too heavy

2.0 tok/s

96800 ms

Agentic Coding

Too heavy

2.0 tok/s

140800 ms

Reasoning

Too heavy

2.0 tok/s

114400 ms

RAG

Too heavy

2.0 tok/s

176000 ms

DeepSeek Coder V2 236B inference speed — tokens per second by GPU & Mac

GPU / Mac

Memory

Quant

Speed (tok/s)

Fits?

Mac Studio M3 Ultra 256GB

256 GB

Q4_K_M

11.7

Too big

Mac Studio M2 Ultra 128GB

128 GB

Q4_K_M

6.1

Too big

Mac Studio M1 Ultra 128GB

128 GB

Q4_K_M

5.7

Too big

MacBook Pro M4 Max 128GB

128 GB

Q4_K_M

4.5

Too big

MacBook Pro M4 Max 64GB

64 GB

Q4_K_M

4.5

Too big

RTX 5090 32GB

32 GB

Q4_K_M

3.4

Too big

2× RX 7900 XTX 24GB

48 GB

Q4_K_M

3.3

Too big

MacBook Pro M3 Max 64GB

64 GB

Q4_K_M

3.1

Too big

MacBook Pro M1 Max 64GB

64 GB

Q4_K_M

2.9

Too big

MacBook Pro M4 Pro 48GB

48 GB

Q4_K_M

2.7

Too big

2× RTX 4090 24GB

48 GB

Q4_K_M

2.3

Too big

RTX 4090 24GB

24 GB

Q4_K_M

2.2

Too big

RTX 4080 Super 16GB

16 GB

Q4_K_M

2.0

Too big

RTX 3090 24GB

24 GB

Q4_K_M

2.0

Too big

RTX 4070 12GB

12 GB

Q4_K_M

2.0

Too big

RTX 3060 12GB

12 GB

Q4_K_M

2.0

Too big

RTX 4060 8GB

8 GB

Q4_K_M

2.0

Too big

RX 7900 XTX 24GB

24 GB

Q4_K_M

2.0

Too big

2× RTX 3090 24GB

48 GB

Q4_K_M

2.0

Too big

4× RTX 3060 12GB

48 GB

Q4_K_M

2.0

Too big

Quant

Bits

VRAM

Quality

Fit

Q2_K

92.0 GB

Low

Q3_K_S

115.6 GB

Low

NVFP4

132.2 GB

Medium

Q4_K_M

144.0 GB

Medium

Q5_K_M

169.9 GB

High

Q6_K

193.5 GB

High

Q8_0

252.5 GB

Very High

F16

483.8 GB

Maximum

Frequently asked questions

Can RTX A5000 24GB run DeepSeek Coder V2 236B?

No, DeepSeek Coder V2 236B requires more memory than RTX A5000 24GB provides.

How much VRAM does DeepSeek Coder V2 236B need?

DeepSeek Coder V2 236B (236B parameters) requires approximately 205.9 GB of memory with Q4_K_M quantization.

What is the best quantization for DeepSeek Coder V2 236B?

The recommended quantization for DeepSeek Coder V2 236B is Q4_K_M, which balances quality and memory efficiency.

What speed will DeepSeek Coder V2 236B run at on RTX A5000 24GB?

On RTX A5000 24GB, DeepSeek Coder V2 236B achieves approximately 2.0 tokens per second decode speed with a time-to-first-token of 96800ms using Q4_K_M quantization.

Can RTX A5000 24GB run DeepSeek Coder V2 236B for coding?

For coding workloads, DeepSeek Coder V2 236B on RTX A5000 24GB receives a F grade with 2.0 tok/s and 4K context.

What context window can DeepSeek Coder V2 236B use on RTX A5000 24GB?

On RTX A5000 24GB, DeepSeek Coder V2 236B can safely use up to 4K 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 236B feels slow on RTX A5000 24GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.