Can speechless zephyr code functionary 7b run on RTX 2070 Super 8GB?

YES — Tight Fit

C53Usable

Estimated from fit model

speechless zephyr code functionary 7b needs ~7.1 GB VRAM. RTX 2070 Super 8GB has 8.0 GB. With Q4_K_M quantization, expect ~64 tok/s.

Runtime: OllamaCapacity: TightBandwidth: 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) — 7.1 GB, 64.0 tok/s, Tight fit

7.1 GB required8.0 GB available

89% VRAM used

Fit status

Tight fit

Decode

64.0 tok/s

TTFT

3025 ms

Safe context

34K

Memory

7.1 GB / 8.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime1.2 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsspeechless zephyr code functionary 7b on RTX 2070 Super 8GB
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: 64.0 tok/s decode · 3.0s TTFT (warm) · 160 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Tight fit	64.0 tok/s	1650 ms	34K
Coding	C	Tight fit	64.0 tok/s	3025 ms	34K
Agentic Coding	C	Runs with offload	64.0 tok/s	4400 ms	34K
Reasoning	C	Tight fit	64.0 tok/s	3575 ms	34K
RAG	C	Runs with offload	64.0 tok/s	5500 ms	34K

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 2070 Super 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C53
Q3_K_S	3	3.4 GB	Low	C53
NVFP4	4	3.9 GB	Medium	C53
Q4_K_M	4	4.3 GB	Medium	C53
Q5_K_MBest for your GPU	5	5.0 GB	High	C53
Q6_K	6	5.7 GB	High	F0
Q8_0	8	7.5 GB	Very High	F0
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

Upgrade options

Hardware that runs speechless zephyr code functionary 7b well

RTX 3060 12GBBudget pick

12 GB VRAM (+4)

Adds memory headroom for longer context windows and future model growth.55.6 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$329 MSRP

RTX 5070 12GBBest value

12 GB VRAM (+4)672 GB/s (+224)

Raises estimated decode speed by about 53%.98 tok/s decode

Raises estimated decode speed by about 53%.

Adds memory headroom for longer context windows and future model growth.

~$549 MSRP

RTX 4070 Super 12GBNVIDIA upgrade

12 GB VRAM (+4)504 GB/s (+56)

Raises estimated decode speed by about 42%.90.9 tok/s decode

Raises estimated decode speed by about 42%.

Adds memory headroom for longer context windows and future model growth.

~$599 MSRP

Frequently asked questions

Can RTX 2070 Super 8GB run speechless zephyr code functionary 7b?

Yes, RTX 2070 Super 8GB can run speechless zephyr code functionary 7b with a C grade (Tight fit). Expected decode speed: 64.0 tok/s.

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

speechless zephyr code functionary 7b (7B parameters) requires approximately 7.1 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 2070 Super 8GB?

On RTX 2070 Super 8GB, speechless zephyr code functionary 7b achieves approximately 64.0 tokens per second decode speed with a time-to-first-token of 3025ms using Q4_K_M quantization.

Can RTX 2070 Super 8GB run speechless zephyr code functionary 7b for coding?

For coding workloads, speechless zephyr code functionary 7b on RTX 2070 Super 8GB receives a C grade with 64.0 tok/s and 34K context.

What context window can speechless zephyr code functionary 7b use on RTX 2070 Super 8GB?

On RTX 2070 Super 8GB, speechless zephyr code functionary 7b can safely use up to 34K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 2070 Super 8GB See all hardware for speechless zephyr code functionary 7b

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<iframe src="https://willitrunai.com/embed/hf-uukuguy--speechless-zephyr-code-functionary-7b-on-rtx-2070-super-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Tight fit

64.0 tok/s

1650 ms

34K

Coding

Tight fit

64.0 tok/s

3025 ms

34K

Agentic Coding

Runs with offload

64.0 tok/s

4400 ms

34K

Reasoning

Tight fit

64.0 tok/s

3575 ms

34K

RAG

Runs with offload

64.0 tok/s

5500 ms

34K

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

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

Quantization options

How speechless zephyr code functionary 7b (7B params) fits at each quantization level on RTX 2070 Super 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C53
Q3_K_S	3	3.4 GB	Low	C53
NVFP4	4	3.9 GB	Medium	C53
Q4_K_M	4	4.3 GB	Medium	C53
Q5_K_MBest for your GPU	5	5.0 GB	High	C53
Q6_K	6	5.7 GB	High	F0
Q8_0	8	7.5 GB	Very High	F0
F16	16	14.3 GB	Maximum	F0