Can gemma 3 27b it run on GTX 1070 Ti 8GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

gemma 3 27b it needs ~21.6 GB but GTX 1070 Ti 8GB only has 8.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: LowStack: BasicBottleneck: Memory capacity

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) — 21.6 GB, exceeds 8.0 GB available

21.6 GB required8.0 GB available

270% VRAM needed

13.6 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.0 tok/s

TTFT

96800 ms

Safe context

Memory

21.6 GB / 8.0 GB

Offload

60%

Memory breakdown

Weights16.5 GB

KV Cache3.2 GB

Runtime1.2 GB

Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsgemma 3 27b it on GTX 1070 Ti 8GB
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: 2.0 tok/s decode · 96.8s TTFT (warm) · 5 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 21.6 GB, but this setup only exposes 8.0 GB of usable VRAM.

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

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	2.0 tok/s	52800 ms	4K
Coding	F	Too heavy	2.0 tok/s	96800 ms	4K
Agentic Coding	F	Too heavy	2.0 tok/s	140800 ms	4K
Reasoning	F	Too heavy	2.0 tok/s	114400 ms	4K
RAG	F	Too heavy	2.0 tok/s	176000 ms	4K

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 GTX 1070 Ti 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	10.5 GB	Low	F0
Q3_K_S	3	13.2 GB	Low	F0
NVFP4	4	15.1 GB	Medium	F0
Q4_K_M	4	16.5 GB	Medium	F0
Q5_K_M	5	19.4 GB	High	F0
Q6_K	6	22.1 GB	High	F0
Q8_0	8	28.9 GB	Very High	F0
F16	16	55.4 GB	Maximum	F0

Opções de upgrade

Hardware que roda bem gemma 3 27b it

RTX 4000 Ada 20GBUpgrade NVIDIA

20 GB VRAM (+12)360 GB/s (+104)

Makes the model fit on the accelerator instead of staying completely out of reach.9.7 tok/s decodificação

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 385%.

~$1,250 MSRP

RTX 3090 24GBOpção econômica

24 GB VRAM (+16)936 GB/s (+680)

Makes the model fit on the accelerator instead of staying completely out of reach.39.8 tok/s decodificação

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,499 MSRP

RTX 4090 24GBMelhor custo-benefício

24 GB VRAM (+16)1008 GB/s (+752)

Makes the model fit on the accelerator instead of staying completely out of reach.46.5 tok/s decodificação

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,599 MSRP

Frequently asked questions

Can GTX 1070 Ti 8GB run gemma 3 27b it?

No, gemma 3 27b it requires more memory than GTX 1070 Ti 8GB provides.

How much VRAM does gemma 3 27b it need?

gemma 3 27b it (27B parameters) requires approximately 21.6 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 GTX 1070 Ti 8GB?

On GTX 1070 Ti 8GB, gemma 3 27b it achieves approximately 2.0 tokens per second decode speed with a time-to-first-token of 96800ms using Q4_K_M quantization.

Can GTX 1070 Ti 8GB run gemma 3 27b it for coding?

For coding workloads, gemma 3 27b it on GTX 1070 Ti 8GB receives a F grade with 2.0 tok/s and 4K context.

What context window can gemma 3 27b it use on GTX 1070 Ti 8GB?

On GTX 1070 Ti 8GB, gemma 3 27b it can safely use up to 4K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if gemma 3 27b it feels slow on GTX 1070 Ti 8GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

See all results for GTX 1070 Ti 8GB See all hardware for gemma 3 27b it

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