Can gemma 3 27b it run on NVIDIA A40 48GB?

YES — Runs Great

C50Usable

Estimated from fit model

gemma 3 27b it needs ~25.6 GB VRAM. NVIDIA A40 48GB has 48.0 GB. With Q4_K_M quantization, expect ~33 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 25.6 GB, 33.0 tok/s, Runs well

25.6 GB required48.0 GB available

53% VRAM used

Fit status

Runs well

Decode

33.0 tok/s

TTFT

5873 ms

Safe context

129K

Memory

25.6 GB / 48.0 GB

Memory breakdown

Weights16.5 GB

KV Cache3.2 GB

Runtime1.2 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsgemma 3 27b it on NVIDIA A40 48GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.

When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.

Key benefits of running locally:
  - Full privacy: your prompts never leave your machine
  - No per-token cost or rate limits
  - Works offline once the model is downloaded
  - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:

  Model size     Q4 (4-bit)    Q8 (8-bit)    FP16
  7B params      ~4.3 GB       ~7.5 GB       ~14 GB
  13B params     ~7.9 GB       ~13.9 GB      ~26 GB
  70B params     ~42.7 GB      ~74.9 GB      ~140 GB

Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 33.0 tok/s decode · 5.9s TTFT (warm) · 82 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	33.0 tok/s	3204 ms	129K
Coding	C	Runs well	33.0 tok/s	5873 ms	129K
Agentic Coding	C	Runs well	33.0 tok/s	8543 ms	129K
Reasoning	C	Runs well	33.0 tok/s	6941 ms	129K
RAG	C	Runs well	33.0 tok/s	10679 ms	129K

Inference speed

gemma 3 27b it inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for gemma 3 27b it at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~73 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	72.9	Fits
RTX 4090 24GB	24 GB	Q4_K_M	46.5	Offloads
RX 7900 XTX 24GB	24 GB	Q4_K_M	42.0	Offloads
RTX 3090 24GB	24 GB	Q4_K_M	39.8	Offloads
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	33.8	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	33.4	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	33.4	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	28.2	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	26.7	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	21.1	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	14.6	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	13.7	Too big
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	13.4	Fits
RTX 4070 12GB	12 GB	Q4_K_M	4.8	Too big
RTX 3060 12GB	12 GB	Q4_K_M	3.0	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.0	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 gemma 3 27b it (27B params) fits at each quantization level on NVIDIA A40 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	10.5 GB	Low	C43
Q3_K_S	3	13.2 GB	Low	C44
NVFP4	4	15.1 GB	Medium	C45
Q4_K_M	4	16.5 GB	Medium	C45
Q5_K_M	5	19.4 GB	High	C46
Q6_K	6	22.1 GB	High	C47
Q8_0Best for your GPU	8	28.9 GB	Very High	C48
F16	16	55.4 GB	Maximum	F0

Get started

Copy-paste commands to run gemma 3 27b it on your machine.

Run

lms load hf-unsloth--gemma-3-27b-it-gguf && lms server start

Upgrade options

Hardware that runs gemma 3 27b it well

AMD Instinct MI210 64GBBudget pick

64 GB VRAM (+16)1638 GB/s (+942)

Raises estimated decode speed by about 105%.67.6 tok/s decode

Raises estimated decode speed by about 105%.

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

~$10,000 MSRP

Frequently asked questions

Can NVIDIA A40 48GB run gemma 3 27b it?

Yes, NVIDIA A40 48GB can run gemma 3 27b it with a C grade (Runs well). Expected decode speed: 33.0 tok/s.

How much VRAM does gemma 3 27b it need?

gemma 3 27b it (27B parameters) requires approximately 25.6 GB of memory with Q4_K_M quantization.

What is the best quantization for gemma 3 27b it?

The recommended quantization for gemma 3 27b it is Q4_K_M, which balances quality and memory efficiency.

What speed will gemma 3 27b it run at on NVIDIA A40 48GB?

On NVIDIA A40 48GB, gemma 3 27b it achieves approximately 33.0 tokens per second decode speed with a time-to-first-token of 5873ms using Q4_K_M quantization.

Can NVIDIA A40 48GB run gemma 3 27b it for coding?

For coding workloads, gemma 3 27b it on NVIDIA A40 48GB receives a C grade with 33.0 tok/s and 129K context.

What context window can gemma 3 27b it use on NVIDIA A40 48GB?

On NVIDIA A40 48GB, gemma 3 27b it can safely use up to 129K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA A40 48GB See all hardware for gemma 3 27b it

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<iframe src="https://willitrunai.com/embed/hf-unsloth--gemma-3-27b-it-gguf-on-a40-48gb" 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

Runs well

33.0 tok/s

3204 ms

129K

Coding

Runs well

33.0 tok/s

5873 ms

129K

Agentic Coding

Runs well

33.0 tok/s

8543 ms

129K

Reasoning

Runs well

33.0 tok/s

6941 ms

129K

RAG

Runs well

33.0 tok/s

10679 ms

129K

gemma 3 27b it inference speed — tokens per second by GPU & Mac

GPU / Mac

Memory

Quant

Speed (tok/s)

Fits?

RTX 5090 32GB

32 GB

Q4_K_M

72.9

Fits

RTX 4090 24GB

24 GB

Q4_K_M

46.5

Offloads

RX 7900 XTX 24GB

24 GB

Q4_K_M

42.0

Offloads

RTX 3090 24GB

24 GB

Q4_K_M

39.8

Offloads

Mac Studio M3 Ultra 256GB

256 GB

Q4_K_M

33.8

Fits

MacBook Pro M4 Max 128GB

128 GB

Q4_K_M

33.4

Fits

MacBook Pro M4 Max 64GB

64 GB

Q4_K_M

33.4

Fits

Mac Studio M2 Ultra 128GB

128 GB

Q4_K_M

28.2

Fits

Mac Studio M1 Ultra 128GB

128 GB

Q4_K_M

26.7

Fits

MacBook Pro M4 Pro 48GB

48 GB

Q4_K_M

21.1

Fits

MacBook Pro M3 Max 64GB

64 GB

Q4_K_M

14.6

Fits

RTX 4080 Super 16GB

16 GB

Q4_K_M

13.7

Too big

MacBook Pro M1 Max 64GB

64 GB

Q4_K_M

13.4

Fits

RTX 4070 12GB

12 GB

Q4_K_M

4.8

Too big

RTX 3060 12GB

12 GB

Q4_K_M

3.0

Too big

RTX 4060 8GB

8 GB

Q4_K_M

2.0

Too big

Quantization options

How gemma 3 27b it (27B params) fits at each quantization level on NVIDIA A40 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	10.5 GB	Low	C43
Q3_K_S	3	13.2 GB	Low	C44
NVFP4	4	15.1 GB	Medium	C45
Q4_K_M	4	16.5 GB	Medium	C45
Q5_K_M	5	19.4 GB	High	C46
Q6_K	6	22.1 GB	High	C47
Q8_0Best for your GPU	8	28.9 GB	Very High	C48
F16	16	55.4 GB	Maximum	F0

Frequently asked questions

Can NVIDIA A40 48GB run gemma 3 27b it?

Yes, NVIDIA A40 48GB can run gemma 3 27b it with a C grade (Runs well). Expected decode speed: 33.0 tok/s.

How much VRAM does gemma 3 27b it need?

gemma 3 27b it (27B parameters) requires approximately 25.6 GB of memory with Q4_K_M quantization.

What is the best quantization for gemma 3 27b it?

The recommended quantization for gemma 3 27b it is Q4_K_M, which balances quality and memory efficiency.

What speed will gemma 3 27b it run at on NVIDIA A40 48GB?

On NVIDIA A40 48GB, gemma 3 27b it achieves approximately 33.0 tokens per second decode speed with a time-to-first-token of 5873ms using Q4_K_M quantization.

Can NVIDIA A40 48GB run gemma 3 27b it for coding?

For coding workloads, gemma 3 27b it on NVIDIA A40 48GB receives a C grade with 33.0 tok/s and 129K context.

What context window can gemma 3 27b it use on NVIDIA A40 48GB?

On NVIDIA A40 48GB, gemma 3 27b it can safely use up to 129K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.