Can Granite Code 34B run on RX 5600 XT 6GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

Granite Code 34B needs ~25.9 GB but RX 5600 XT 6GB only has 6.0 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: LowStack: 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) — 25.9 GB, exceeds 6.0 GB available

25.9 GB required6.0 GB available

432% VRAM needed

19.9 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.0 tok/s

TTFT

96800 ms

Safe context

Memory

25.9 GB / 6.0 GB

Offload

80%

Memory breakdown

Weights20.7 GB

KV Cache3.7 GB

Runtime0.9 GB

Headroom0.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsGranite Code 34B on RX 5600 XT 6GB
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 25.9 GB, but this setup only exposes 6.0 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

Quantization options

How Granite Code 34B (34B params) fits at each quantization level on RX 5600 XT 6GB (6.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.3 GB	Low	F0
Q3_K_S	3	16.7 GB	Low	F0
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

アップグレードオプション

Granite Code 34Bを快適に動かすハードウェア

RX 7900 XTX 24GBコスパ最良

24 GB VRAM (+18)960 GB/s (+672)

Makes the model fit on the accelerator instead of staying completely out of reach.20 tok/sデコード

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

Raises estimated decode speed by about 900%.

〜$999 MSRP

Radeon AI PRO R9700 32GBコスパ重視

32 GB VRAM (+26)640 GB/s (+352)

Makes the model fit on the accelerator instead of staying completely out of reach.19.7 tok/sデコード

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.

〜$1,899 MSRP

Radeon Pro W6800 32GBAMDアップグレード

32 GB VRAM (+26)512 GB/s (+224)

Makes the model fit on the accelerator instead of staying completely out of reach.15 tok/sデコード

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.

〜$2,249 MSRP

NVIDIA A100 40GB最大の飛躍

40 GB VRAM (+34)1555 GB/s (+1267)

Makes the model fit on the accelerator instead of staying completely out of reach.68.2 tok/sデコード

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.

〜$10,000 MSRP

Frequently asked questions

Can RX 5600 XT 6GB run Granite Code 34B?

No, Granite Code 34B requires more memory than RX 5600 XT 6GB provides.

How much VRAM does Granite Code 34B need?

Granite Code 34B (34B parameters) requires approximately 25.9 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 RX 5600 XT 6GB?

On RX 5600 XT 6GB, Granite Code 34B achieves approximately 2.0 tokens per second decode speed with a time-to-first-token of 96800ms using Q4_K_M quantization.

Can RX 5600 XT 6GB run Granite Code 34B for coding?

For coding workloads, Granite Code 34B on RX 5600 XT 6GB receives a F grade with 2.0 tok/s and 4K context.

What context window can Granite Code 34B use on RX 5600 XT 6GB?

On RX 5600 XT 6GB, 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 RX 5600 XT 6GB?

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 RX 5600 XT 6GB See all hardware for Granite Code 34B

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