Can Granite Code 34B run on RTX 5000 Ada 32GB?

YES — Tight Fit

A76Great

Estimated from fit model

Granite Code 34B needs ~28.8 GB VRAM. RTX 5000 Ada 32GB has 32.0 GB. With Q4_K_M quantization, expect ~24 tok/s.

Runtime: OllamaCapacity: TightBandwidth: MediumStack: BasicBottleneck: 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) — 28.8 GB, 24.1 tok/s, Tight fit

28.8 GB required32.0 GB available

90% VRAM used

Fit status

Tight fit

Decode

24.1 tok/s

TTFT

8043 ms

Safe context

Memory

28.8 GB / 32.0 GB

Memory breakdown

Weights20.7 GB

KV Cache3.7 GB

Runtime1.2 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsGranite Code 34B on RTX 5000 Ada 32GB
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: 24.1 tok/s decode · 8.0s TTFT (warm) · 60 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	A	Tight fit	24.1 tok/s	4387 ms	8K
Coding	A	Tight fit	24.1 tok/s	8043 ms	8K
Agentic Coding	A	Runs with offload (needs ~0.3 GB host RAM)	17.5 tok/s	16079 ms	8K
Reasoning	A	Tight fit	24.1 tok/s	9506 ms	8K
RAG	A	Runs with offload (needs ~0.3 GB host RAM)	17.5 tok/s	20099 ms	8K

Inference speed

Granite Code 34B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Granite Code 34B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~63 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	62.7	Tight
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	31.4	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	31.4	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	29.1	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	24.2	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	23.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	21.7	Heavy offload
RX 7900 XTX 24GB	24 GB	Q4_K_M	20.0	Heavy offload
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	19.8	Tight
RTX 3090 24GB	24 GB	Q4_K_M	18.6	Heavy offload
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	12.5	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	11.5	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	7.8	Too big
RTX 4070 12GB	12 GB	Q4_K_M	3.0	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

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 Granite Code 34B (34B params) fits at each quantization level on RTX 5000 Ada 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.3 GB	Low	A75
Q3_K_S	3	16.7 GB	Low	A76
NVFP4	4	19.0 GB	Medium	A76
Q4_K_M	4	20.7 GB	Medium	A76
Q5_K_MBest for your GPU	5	24.5 GB	High	A75
Q6_K	6	27.9 GB	High	F0
Q8_0	8	36.4 GB	Very High	F0
F16	16	69.7 GB	Maximum	F0

Get started

Copy-paste commands to run Granite Code 34B on your machine.

Run

ollama run granite-code:34b

Your hardware

More models your RTX 5000 Ada 32GB can run

Model	Params	Grade	Decode
Qwen 3.6 35B A3B	35B	S	58.6 tok/s
Qwen 3.5 35B A3B	35B	S	63.7 tok/s
Kimi Linear 48B A3B	48B	B	10 tok/s
Ornith 1.0 35B A3B	35.1B	S	63.5 tok/s

Frequently asked questions

Can RTX 5000 Ada 32GB run Granite Code 34B?

Yes, RTX 5000 Ada 32GB can run Granite Code 34B with a A grade (Tight fit). Expected decode speed: 24.1 tok/s.

How much VRAM does Granite Code 34B need?

Granite Code 34B (34B parameters) requires approximately 28.8 GB of memory with Q4_K_M quantization.

What is the best quantization for Granite Code 34B?

The recommended quantization for Granite Code 34B is Q4_K_M, which balances quality and memory efficiency.

What speed will Granite Code 34B run at on RTX 5000 Ada 32GB?

On RTX 5000 Ada 32GB, Granite Code 34B achieves approximately 24.1 tokens per second decode speed with a time-to-first-token of 8043ms using Q4_K_M quantization.

Can RTX 5000 Ada 32GB run Granite Code 34B for coding?

For coding workloads, Granite Code 34B on RTX 5000 Ada 32GB receives a A grade with 24.1 tok/s and 8K context.

What context window can Granite Code 34B use on RTX 5000 Ada 32GB?

On RTX 5000 Ada 32GB, Granite Code 34B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

See all results for RTX 5000 Ada 32GB See all hardware for Granite Code 34B

Embed this result▼

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<iframe src="https://willitrunai.com/embed/granite-code-34b-on-rtx-5000-ada-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Tight fit

24.1 tok/s

4387 ms

Coding

Tight fit

24.1 tok/s

8043 ms

Agentic Coding

Runs with offload (needs ~0.3 GB host RAM)

17.5 tok/s

16079 ms

Reasoning

Tight fit

24.1 tok/s

9506 ms

RAG

Runs with offload (needs ~0.3 GB host RAM)

17.5 tok/s

20099 ms

Granite Code 34B inference speed — tokens per second by GPU & Mac

GPU / Mac

Memory

Quant

Speed (tok/s)

Fits?

RTX 5090 32GB

32 GB

Q4_K_M

62.7

Tight

MacBook Pro M4 Max 128GB

128 GB

Q4_K_M

31.4

Fits

MacBook Pro M4 Max 64GB

64 GB

Q4_K_M

31.4

Fits

Mac Studio M3 Ultra 256GB

256 GB

Q4_K_M

29.1

Fits

Mac Studio M2 Ultra 128GB

128 GB

Q4_K_M

24.2

Fits

Mac Studio M1 Ultra 128GB

128 GB

Q4_K_M

23.0

Fits

RTX 4090 24GB

24 GB

Q4_K_M

21.7

Heavy offload

RX 7900 XTX 24GB

24 GB

Q4_K_M

20.0

Heavy offload

MacBook Pro M4 Pro 48GB

48 GB

Q4_K_M

19.8

Tight

RTX 3090 24GB

24 GB

Q4_K_M

18.6

Heavy offload

MacBook Pro M3 Max 64GB

64 GB

Q4_K_M

12.5

Fits

MacBook Pro M1 Max 64GB

64 GB

Q4_K_M

11.5

Fits

RTX 4080 Super 16GB

16 GB

Q4_K_M

7.8

Too big

RTX 4070 12GB

12 GB

Q4_K_M

3.0

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

Quantization options

How Granite Code 34B (34B params) fits at each quantization level on RTX 5000 Ada 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.3 GB	Low	A75
Q3_K_S	3	16.7 GB	Low	A76
NVFP4	4	19.0 GB	Medium	A76
Q4_K_M	4	20.7 GB	Medium	A76
Q5_K_MBest for your GPU	5	24.5 GB	High	A75
Q6_K	6	27.9 GB	High	F0
Q8_0	8	36.4 GB	Very High	F0
F16	16	69.7 GB	Maximum	F0

Model

Params

Grade

Decode

Capabilities

Qwen 3.6 35B A3B

35B

58.6 tok/s

Qwen 3.5 35B A3B

35B

63.7 tok/s

Kimi Linear 48B A3B

48B

10 tok/s

Ornith 1.0 35B A3B

35.1B

63.5 tok/s

Frequently asked questions

Can RTX 5000 Ada 32GB run Granite Code 34B?

Yes, RTX 5000 Ada 32GB can run Granite Code 34B with a A grade (Tight fit). Expected decode speed: 24.1 tok/s.

How much VRAM does Granite Code 34B need?

Granite Code 34B (34B parameters) requires approximately 28.8 GB of memory with Q4_K_M quantization.

What is the best quantization for Granite Code 34B?

The recommended quantization for Granite Code 34B is Q4_K_M, which balances quality and memory efficiency.

What speed will Granite Code 34B run at on RTX 5000 Ada 32GB?

On RTX 5000 Ada 32GB, Granite Code 34B achieves approximately 24.1 tokens per second decode speed with a time-to-first-token of 8043ms using Q4_K_M quantization.

Can RTX 5000 Ada 32GB run Granite Code 34B for coding?

For coding workloads, Granite Code 34B on RTX 5000 Ada 32GB receives a A grade with 24.1 tok/s and 8K context.

What context window can Granite Code 34B use on RTX 5000 Ada 32GB?

On RTX 5000 Ada 32GB, Granite Code 34B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.