Can Granite Code 34B run on RTX 6000 Ada 48GB?

YES — Runs Great

A80Great
Estimated from fit model

Granite Code 34B needs ~30.4 GB VRAM. RTX 6000 Ada 48GB has 48.0 GB. With Q4_K_M quantization, expect ~41 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: Balanced
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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) 30.4 GB, 41.1 tok/s, Runs well
30.4 GB required48.0 GB available
63% VRAM used

Fit status

Runs well

Decode

41.1 tok/s

TTFT

4708 ms

Safe context

8K

Memory

30.4 GB / 48.0 GB

Memory breakdown

Weights20.7 GB
KV Cache3.7 GB
Runtime1.2 GB
Headroom4.8 GB

See how fast it feels

See how fast it feelsGranite Code 34B on RTX 6000 Ada 48GB
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: 41.1 tok/s decode · 4.7s TTFT (warm) · 103 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

WorkloadGradeFitDecodeTTFTContext
ChatARuns well41.1 tok/s2568 ms8K
CodingARuns well41.1 tok/s4708 ms8K
Agentic CodingARuns well41.1 tok/s6848 ms8K
ReasoningARuns well41.1 tok/s5564 ms8K
RAGARuns well41.1 tok/s8561 ms8K

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 / MacMemoryQuantSpeed (tok/s)Fits?
NVIDIARTX 5090 32GB
32 GBQ4_K_M62.7Tight
MacBook Pro M4 Max 128GB
128 GBQ4_K_M31.4Fits
MacBook Pro M4 Max 64GB
64 GBQ4_K_M31.4Fits
Mac Studio M3 Ultra 256GB
256 GBQ4_K_M29.1Fits
Mac Studio M2 Ultra 128GB
128 GBQ4_K_M24.2Fits
Mac Studio M1 Ultra 128GB
128 GBQ4_K_M23.0Fits
NVIDIARTX 4090 24GB
24 GBQ4_K_M21.7Heavy offload
RX 7900 XTX 24GB
24 GBQ4_K_M20.0Heavy offload
MacBook Pro M4 Pro 48GB
48 GBQ4_K_M19.8Tight
NVIDIARTX 3090 24GB
24 GBQ4_K_M18.6Heavy offload
MacBook Pro M3 Max 64GB
64 GBQ4_K_M12.5Fits
MacBook Pro M1 Max 64GB
64 GBQ4_K_M11.5Fits
NVIDIARTX 4080 Super 16GB
16 GBQ4_K_M7.8Too big
NVIDIARTX 4070 12GB
12 GBQ4_K_M3.0Too big
NVIDIARTX 3060 12GB
12 GBQ4_K_M2.0Too big
NVIDIARTX 4060 8GB
8 GBQ4_K_M2.0Too 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 6000 Ada 48GB (48.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
13.3 GB
LowA71
Q3_K_S
3
16.7 GB
LowA72
NVFP4
4
19.0 GB
MediumA73
Q4_K_M
4
20.7 GB
MediumA73
Q5_K_M
5
24.5 GB
HighA75
Q6_K
6
27.9 GB
HighA75
Q8_0Best for your GPU
8
36.4 GB
Very HighA75
F16
16
69.7 GB
MaximumF0

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 6000 Ada 48GB can run

ModelParamsGradeDecodeCapabilities
AlibabaQwen 3.6 35B A3B35BS100 tok/s
AlibabaQwen 3.5 35B A3B35BS108.8 tok/s
AlibabaQwen 2.5 VL 72B72BA11.1 tok/s
AlibabaQwen3-Coder-Next80BA28.5 tok/s
MetaLlama 3.3 70B70BA11.9 tok/s

Frequently asked questions

Can RTX 6000 Ada 48GB run Granite Code 34B?

Yes, RTX 6000 Ada 48GB can run Granite Code 34B with a A grade (Runs well). Expected decode speed: 41.1 tok/s.

How much VRAM does Granite Code 34B need?

Granite Code 34B (34B parameters) requires approximately 30.4 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 6000 Ada 48GB?

On RTX 6000 Ada 48GB, Granite Code 34B achieves approximately 41.1 tokens per second decode speed with a time-to-first-token of 4708ms using Q4_K_M quantization.

Can RTX 6000 Ada 48GB run Granite Code 34B for coding?

For coding workloads, Granite Code 34B on RTX 6000 Ada 48GB receives a A grade with 41.1 tok/s and 8K context.

What context window can Granite Code 34B use on RTX 6000 Ada 48GB?

On RTX 6000 Ada 48GB, 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 6000 Ada 48GBSee all hardware for Granite Code 34B
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