Can Granite Code 20B run on RTX 4070 12GB?

YES — With Q2_K

B70Good

Estimated from fit model

Granite Code 20B needs ~13.4 GB VRAM. RTX 4070 12GB has 12.0 GB. With Q2_K quantization, expect ~27 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: MediumStack: 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.

Granite Code 20B at Q4_K_M needs 17.8 GB — too much for RTX 4070 12GB (12.0 GB). Runs at Q2_K (13.4 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 17.8 GB, exceeds 12.0 GB available

17.8 GB required12.0 GB available

148% VRAM needed

5.8 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

11.0 tok/s

TTFT

17636 ms

Safe context

Memory

17.8 GB / 12.0 GB

Offload

30%

Memory breakdown

Weights12.2 GB

KV Cache3.2 GB

Runtime1.2 GB

Headroom1.2 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsGranite Code 20B on RTX 4070 12GB
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: 11.0 tok/s decode · 17.6s TTFT (warm) · 27 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 0.8 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	F	Too heavy	13.4 tok/s	7901 ms	4K
Coding	F	Too heavy	11.0 tok/s	17636 ms	4K
Agentic Coding	F	Too heavy	7.8 tok/s	36252 ms	4K
Reasoning	F	Too heavy	11.0 tok/s	20843 ms	4K
RAG	F	Too heavy	7.8 tok/s	45315 ms	4K

Inference speed

Granite Code 20B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Granite Code 20B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~106 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	106.3	Fits
RTX 4090 24GB	24 GB	Q4_K_M	67.8	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	61.2	Fits
RTX 3090 24GB	24 GB	Q4_K_M	58.0	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	49.3	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	41.1	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	39.0	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	38.4	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	38.4	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	31.0	Heavy offload
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	24.2	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	21.2	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	19.5	Fits
RTX 4070 12GB	12 GB	Q4_K_M	11.0	Too big
RTX 3060 12GB	12 GB	Q4_K_M	6.9	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.6	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 20B (20B params) fits at each quantization level on RTX 4070 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	7.8 GB	Low	A81
Q3_K_S	3	9.8 GB	Low	F0
NVFP4	4	11.2 GB	Medium	F0
Q4_K_M	4	12.2 GB	Medium	F0
Q5_K_M	5	14.4 GB	High	F0
Q6_K	6	16.4 GB	High	F0
Q8_0	8	21.4 GB	Very High	F0
F16	16	41.0 GB	Maximum	F0

Get started

Copy-paste commands to run Granite Code 20B on your machine.

Run

ollama run granite-code:20b

Opciones de mejora

Hardware que ejecuta bien Granite Code 20B

RTX 5060 Ti 16GBMejor relación calidad-precio

16 GB VRAM (+4)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.14.5 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Sube la velocidad estimada de decodificación alrededor de un 32%.

~$449 MSRP

RTX 4060 Ti 16GBMejora NVIDIA

16 GB VRAM (+4)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.10.7 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$499 MSRP

RTX 4000 Ada 20GBOpción económica

20 GB VRAM (+8)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.24.9 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$1,250 MSRP

RTX 4090 24GBMayor salto

24 GB VRAM (+12)1008 GB/s (+504)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.67.8 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$1,599 MSRP

Frequently asked questions

Can RTX 4070 12GB run Granite Code 20B?

Yes, RTX 4070 12GB can run Granite Code 20B at Q2_K quantization (Very compromised (needs ~0.8 GB host RAM)). The recommended Q4_K_M requires 17.8 GB which exceeds available memory, but at Q2_K it needs only 13.4 GB. Expected decode speed: 26.6 tok/s.

How much VRAM does Granite Code 20B need?

Granite Code 20B (20B parameters) requires approximately 17.8 GB at Q4_K_M quantization. On RTX 4070 12GB, it fits at Q2_K using 13.4 GB.

What is the best quantization for Granite Code 20B?

The recommended quantization is Q4_K_M, but on RTX 4070 12GB the best fitting quantization is Q2_K, which uses 13.4 GB.

What speed will Granite Code 20B run at on RTX 4070 12GB?

On RTX 4070 12GB, Granite Code 20B achieves approximately 26.6 tokens per second decode speed with a time-to-first-token of 7289ms using Q2_K quantization.

Can RTX 4070 12GB run Granite Code 20B for coding?

For coding workloads, Granite Code 20B on RTX 4070 12GB receives a F grade with 11.0 tok/s and 4K context.

What context window can Granite Code 20B use on RTX 4070 12GB?

On RTX 4070 12GB, Granite Code 20B can safely use up to 8K tokens of context at Q2_K quantization. The model's official context limit is 8K, but available memory constrains the safe maximum.

What should I upgrade first if Granite Code 20B feels slow on RTX 4070 12GB?

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 4070 12GB See all hardware for Granite Code 20B

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