Can speechless zephyr code functionary 7b run on NVIDIA A800 80GB?

YES — Runs Great

C46Usable

Estimated from fit model

speechless zephyr code functionary 7b needs ~14.3 GB VRAM. NVIDIA A800 80GB has 80.0 GB. With Q4_K_M quantization, expect ~98 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: 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) — 14.3 GB, 98.0 tok/s, Runs well

14.3 GB required80.0 GB available

18% VRAM used

Fit status

Runs well

Decode

98.0 tok/s

TTFT

1976 ms

Safe context

1.3M

Memory

14.3 GB / 80.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime1.2 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsspeechless zephyr code functionary 7b on NVIDIA A800 80GB
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: 98.0 tok/s decode · 2.0s TTFT (warm) · 245 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	98.0 tok/s	1078 ms	1.3M
Coding	C	Runs well	98.0 tok/s	1976 ms	1.3M
Agentic Coding	C	Runs well	98.0 tok/s	2873 ms	1.3M
Reasoning	C	Runs well	98.0 tok/s	2335 ms	1.3M
RAG	C	Runs well	98.0 tok/s	3592 ms	1.3M

Quantization options

How speechless zephyr code functionary 7b (7B params) fits at each quantization level on NVIDIA A800 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	D39
Q3_K_S	3	3.4 GB	Low	D39
NVFP4	4	3.9 GB	Medium	D39
Q4_K_M	4	4.3 GB	Medium	D40
Q5_K_M	5	5.0 GB	High	D40
Q6_K	6	5.7 GB	High	D40
Q8_0	8	7.5 GB	Very High	D40
F16Best for your GPU	16	14.3 GB	Maximum	C41

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

Upgrade options

Hardware that runs speechless zephyr code functionary 7b well

Mac Studio M2 Ultra 128GBBudget pick

128 GB Unified (+48)

This setup is broadly balanced for this model.98 tok/s decode

~$3,999 MSRP

Mac Studio M1 Ultra 128GBBest value

128 GB Unified (+48)

This setup is broadly balanced for this model.98 tok/s decode

~$3,999 MSRP

Frequently asked questions

Can NVIDIA A800 80GB run speechless zephyr code functionary 7b?

Yes, NVIDIA A800 80GB can run speechless zephyr code functionary 7b with a C grade (Runs well). Expected decode speed: 98.0 tok/s.

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

speechless zephyr code functionary 7b (7B parameters) requires approximately 14.3 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 NVIDIA A800 80GB?

On NVIDIA A800 80GB, speechless zephyr code functionary 7b achieves approximately 98.0 tokens per second decode speed with a time-to-first-token of 1976ms using Q4_K_M quantization.

Can NVIDIA A800 80GB run speechless zephyr code functionary 7b for coding?

For coding workloads, speechless zephyr code functionary 7b on NVIDIA A800 80GB receives a C grade with 98.0 tok/s and 1.3M context.

What context window can speechless zephyr code functionary 7b use on NVIDIA A800 80GB?

On NVIDIA A800 80GB, speechless zephyr code functionary 7b can safely use up to 1.3M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA A800 80GB See all hardware for speechless zephyr code functionary 7b

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

98.0 tok/s

1078 ms

1.3M

Coding

Runs well

98.0 tok/s

1976 ms

1.3M

Agentic Coding

Runs well

98.0 tok/s

2873 ms

1.3M

Reasoning

Runs well

98.0 tok/s

2335 ms

1.3M

RAG

Runs well

98.0 tok/s

3592 ms

1.3M

Quantization options

How speechless zephyr code functionary 7b (7B params) fits at each quantization level on NVIDIA A800 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	D39
Q3_K_S	3	3.4 GB	Low	D39
NVFP4	4	3.9 GB	Medium	D39
Q4_K_M	4	4.3 GB	Medium	D40
Q5_K_M	5	5.0 GB	High	D40
Q6_K	6	5.7 GB	High	D40
Q8_0	8	7.5 GB	Very High	D40
F16Best for your GPU	16	14.3 GB	Maximum	C41