Can speechless zephyr code functionary 7b run on RTX A2000 12GB?

YES — Runs Great

C53Usable

Estimated from fit model

speechless zephyr code functionary 7b needs ~7.5 GB VRAM. RTX A2000 12GB has 12.0 GB. With Q4_K_M quantization, expect ~53 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: 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) — 7.5 GB, 52.6 tok/s, Runs well

7.5 GB required12.0 GB available

63% VRAM used

Fit status

Runs well

Decode

52.6 tok/s

TTFT

3680 ms

Safe context

104K

Memory

7.5 GB / 12.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime1.2 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsspeechless zephyr code functionary 7b on RTX A2000 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: 52.6 tok/s decode · 3.7s TTFT (warm) · 132 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	C	Runs well	52.6 tok/s	2007 ms	104K
Coding	C	Runs well	52.6 tok/s	3680 ms	104K
Agentic Coding	C	Runs well	52.6 tok/s	5353 ms	104K
Reasoning	C	Runs well	52.6 tok/s	4349 ms	104K
RAG	C	Runs well	52.6 tok/s	6691 ms	104K

Inference speed

speechless zephyr code functionary 7b inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for speechless zephyr code functionary 7b at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~98 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	98.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	98.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	98.0	Fits
RTX 3090 24GB	24 GB	Q4_K_M	98.0	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	98.0	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	98.0	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	98.0	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	98.0	Fits
RTX 4070 12GB	12 GB	Q4_K_M	88.5	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	87.8	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	87.8	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	56.2	Fits
RTX 3060 12GB	12 GB	Q4_K_M	55.6	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	51.5	Fits
RTX 4060 8GB	8 GB	Q4_K_M	46.5	Tight
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	45.3	Fits

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 speechless zephyr code functionary 7b (7B params) fits at each quantization level on RTX A2000 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C49
Q3_K_S	3	3.4 GB	Low	C50
NVFP4	4	3.9 GB	Medium	C50
Q4_K_M	4	4.3 GB	Medium	C51
Q5_K_M	5	5.0 GB	High	C52
Q6_K	6	5.7 GB	High	C52
Q8_0Best for your GPU	8	7.5 GB	Very High	C52
F16	16	14.3 GB	Maximum	F0

Get started

Copy-paste commands to run speechless zephyr code functionary 7b on your machine.

Run

lms load hf-uukuguy--speechless-zephyr-code-functionary-7b && lms server start

Frequently asked questions

Can RTX A2000 12GB run speechless zephyr code functionary 7b?

Yes, RTX A2000 12GB can run speechless zephyr code functionary 7b with a C grade (Runs well). Expected decode speed: 52.6 tok/s.

How much VRAM does speechless zephyr code functionary 7b need?

speechless zephyr code functionary 7b (7B parameters) requires approximately 7.5 GB of memory with Q4_K_M quantization.

What is the best quantization for speechless zephyr code functionary 7b?

The recommended quantization for speechless zephyr code functionary 7b is Q4_K_M, which balances quality and memory efficiency.

What speed will speechless zephyr code functionary 7b run at on RTX A2000 12GB?

On RTX A2000 12GB, speechless zephyr code functionary 7b achieves approximately 52.6 tokens per second decode speed with a time-to-first-token of 3680ms using Q4_K_M quantization.

Can RTX A2000 12GB run speechless zephyr code functionary 7b for coding?

For coding workloads, speechless zephyr code functionary 7b on RTX A2000 12GB receives a C grade with 52.6 tok/s and 104K context.

What context window can speechless zephyr code functionary 7b use on RTX A2000 12GB?

On RTX A2000 12GB, speechless zephyr code functionary 7b can safely use up to 104K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX A2000 12GB See all hardware for speechless zephyr code functionary 7b

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/hf-uukuguy--speechless-zephyr-code-functionary-7b-on-a2000-12gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: