Can Granite Code 34B run on RTX 3090 24GB?

BARELY — Tight on Memory

B66Good

Estimated from fit model

Granite Code 34B needs ~28.0 GB VRAM. RTX 3090 24GB has 24.0 GB. With Q4_K_M quantization, expect ~19 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: HighStack: BasicBottleneck: Host offload

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) — 28.0 GB, 18.6 tok/s, Very compromised (needs ~3 GB host RAM)

28.0 GB required24.0 GB available

117% VRAM needed

4.0 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~3 GB host RAM)

Decode

18.6 tok/s

TTFT

10435 ms

Safe context

Memory

28.0 GB / 24.0 GB

Offload

10%

Memory breakdown

Weights20.7 GB

KV Cache3.7 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsGranite Code 34B on RTX 3090 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: 18.6 tok/s decode · 10.4s TTFT (warm) · 46 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 3.0 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Very compromised (needs ~1.7 GB host RAM)	21.4 tok/s	4937 ms	4K
Coding	B	Very compromised (needs ~3 GB host RAM)	18.6 tok/s	10435 ms	4K
Agentic Coding	F	Too heavy	14.3 tok/s	19661 ms	4K
Reasoning	B	Very compromised (needs ~3 GB host RAM)	18.6 tok/s	12333 ms	4K
RAG	F	Too heavy	14.3 tok/s	24576 ms	4K

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 3090 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.3 GB	Low	A77
Q3_K_SBest for your GPU	3	16.7 GB	Low	A76
NVFP4	4	19.0 GB	Medium	F0
Q4_K_M	4	20.7 GB	Medium	F0
Q5_K_M	5	24.5 GB	High	F0
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

升级选项

能流畅运行 Granite Code 34B 的硬件

RTX 5090 32GB经济之选

32 GB VRAM (+8)1792 GB/s (+856)

Removes host-memory offload, which is usually the single biggest latency and throughput win.62.7 tok/s 解码

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

Raises estimated decode speed by about 237%.

~$1,999 MSRP

RTX PRO 4500 Blackwell 32GB最佳性价比

32 GB VRAM (+8)

Removes host-memory offload, which is usually the single biggest latency and throughput win.39.3 tok/s 解码

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

Raises estimated decode speed by about 111%.

~$2,499 MSRP

RTX 5000 Ada 32GBNVIDIA 升级

32 GB VRAM (+8)

Removes host-memory offload, which is usually the single biggest latency and throughput win.24.1 tok/s 解码

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

Raises estimated decode speed by about 30%.

~$4,000 MSRP

Frequently asked questions

Can RTX 3090 24GB run Granite Code 34B?

Yes, RTX 3090 24GB can run Granite Code 34B with a B grade (Very compromised (needs ~3 GB host RAM)). Expected decode speed: 18.6 tok/s.

How much VRAM does Granite Code 34B need?

Granite Code 34B (34B parameters) requires approximately 28.0 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 3090 24GB?

On RTX 3090 24GB, Granite Code 34B achieves approximately 18.6 tokens per second decode speed with a time-to-first-token of 10435ms using Q4_K_M quantization.

Can RTX 3090 24GB run Granite Code 34B for coding?

For coding workloads, Granite Code 34B on RTX 3090 24GB receives a B grade with 18.6 tok/s and 4K context.

What context window can Granite Code 34B use on RTX 3090 24GB?

On RTX 3090 24GB, Granite Code 34B can safely use up to 4K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

What should I upgrade first if Granite Code 34B feels slow on RTX 3090 24GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

See all results for RTX 3090 24GB See all hardware for Granite Code 34B

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