Computed

Gemma 2 27B Instruct on a GeForce RTX 4070 Ti SUPER

Yes, quantized. Q3_K_M is the largest that fits, leaving room for 6k tokens of context.

Change the numbers →Gemma 2 27B Instruct on every card GeForce RTX 4070 Ti SUPER across every model

Verdict

Q3_K_M

14.7 GiB usable of 16 GB

Weights (Q4_K_M)

15.5 GiB

50.7 GiB at FP16 · 27.2B params · 6 of 6 quant rows are measured files

KV cache

368.0 KiB per token at FP16

46 layers × 16 KV heads × 128 dimensions

Speed ceiling

43 tok/s

672 GB/s ÷ bytes read per token

Every quant, against this card

Weight bytes are the size of the real published file wherever one exists — 6 of these6 rows are measured from bartowski/gemma-2-27b-it-GGUF, the rest computed from the parameter count. Max context is what the KV cache can grow to in whatever memory the weights leave behind, capped at the 8k tokens this model was trained to address.

Bars are the weight bytes at each precision; the dashed line is the usable memory of a GeForce RTX 4070 Ti SUPER. Drawn from the computed byte counts, not sketched.

Precision	Bits/weight	Weights	Source	Fits	Max context	Ceiling
FP16 / BF16	16.00	50.7 GiB	measured file	−36.0 GiB	—	2.1 tok/s offloaded
Q8_0	8.50	27.0 GiB	measured file	−12.2 GiB	—	4.9 tok/s offloaded
Q6_K	6.57	20.8 GiB	measured file	−6.1 GiB	—	7.7 tok/s offloaded
Q5_K_M	5.70	18.1 GiB	measured file	−3.4 GiB	—	10.2 tok/s offloaded
Q4_K_M	4.89	15.5 GiB	measured file	−801 MiB	—	14.9 tok/s offloaded
Q3_K_M	3.94	12.5 GiB	measured file	yes	6,318	43 tok/s

What the context actually costs

The KV cache is 2 · layers · kv_heads · head_dim bytes per token per element — 2 · 46 · 16 · 128 · 2 B = 368.0 KiB. It depends on the key/value head count, not the attention-head count and not the parameter count. This model shares 16 KV heads across 32 query heads, which divides the cache by 2 against multi-head attention.

An upper bound, deliberately. This model's config declares a 4,096-token sliding window but does not state which layers use it, so every layer is costed as full attention here. The real cache is smaller. Filling in the pattern from what the architecture is known to do elsewhere would be a remembered fact, and this page does not publish those.

Context	KV cache (FP16)	KV cache (8-bit)	Plus Q4_K_M weights
4,096	1.4 GiB	736 MiB	16.9 GiB
8,192	2.9 GiB	1.4 GiB	18.4 GiB

The speed ceiling, and where it comes from

Generating one token reads every active weight from memory once. At Q3_K_M that is 12.5 GiB, plus a pass over the KV cache. A GeForce RTX 4070 Ti SUPER moves 672 GB/s (256-bit × 21 Gbps), so the arithmetic ceiling is 43 tok/s. Treat it as a bound, not an estimate: attention overhead, kernel launches and imperfect memory access keep real runtimes at roughly 60–80% of it, and nothing pushes past it.

Where these numbers come from

The model

Parameters: 27,227,128,320
Layers: 46
Attention / KV heads: 32 / 16
Head dimension: 128
Trained context: 8,192
Checkpoint as published: 50.7 GiB

Read from unsloth/gemma-2-27b-it — an ungated mirror of google/gemma-2-27b-it, whose own config cannot be fetched without an access token. The parameter count is the Hub's own total over the tensor shapes, not a figure taken from the model's name.

The accelerator

Memory: 16 GB GDDR6X
Bandwidth: 672 GB/s
Bus: 256-bit × 21 Gbps
Assumed usable: 92% → 14.7 GiB

Capacity and bandwidth from the vendor's specification. The bandwidth figure is checked against the bus width and data rate it derives from. The usable fraction is an assumption, not a spec: a driver context, compute workspace and any attached display come out of the same pool before a weight is loaded.

Questions this pairing answers

How much VRAM does Gemma 2 27B Instruct need?

50.7 GiB for the weights at FP16 — 27.2B parameters at two bytes each — and 15.5 GiB at Q4_K_M. The KV cache is on top of that and is not a fixed number: this model spends 368.0 KiB per token of context, so 8,192 tokens costs a further 2.9 GiB. A GeForce RTX 4070 Ti SUPER makes 14.7 GiB of its 16 GB available on the assumption below.

Can a GeForce RTX 4070 Ti SUPER run Gemma 2 27B Instruct?

Yes, quantized. Q3_K_M is the largest that fits, leaving room for 6k tokens of context. That is the weights and the KV cache together, against 14.7 GiB of usable memory.

How fast will Gemma 2 27B Instruct run on a GeForce RTX 4070 Ti SUPER?

No faster than 43 tokens/second at Q3_K_M, and in practice below it. Decoding is memory-bound: every token reads all 12.5 GiB of weights plus the cache, and this card moves 672 GB/s. That division is the ceiling — no kernel, runtime or driver beats it, and a real runtime typically reaches 60–80% of it.

Why does the context length change how much memory Gemma 2 27B Instruct needs?

Because the KV cache holds one key and one value vector per token, per layer, for the whole conversation, and it is allocated separately from the weights. This model has 46 layers and 16 key/value heads of 128 dimensions, shared across 32 query heads — grouped-query attention, which divides the cache by 2. That works out at 368.0 KiB per token. Parameter count tells you nothing about this number.

The same model on a different card

A different model on the same card

All 62 models on GeForce RTX 4070 Ti SUPER →

Method and limits. Weight bytes are the byte size of the real published file wherever one exists, and the model's exact parameter count times the published llama.cpp bits-per-weight where it does not. That distinction is on every row above and it matters at both ends: a sub-1B model's Q4_K_M file runs a third larger than the nominal figure because k-quants keep its embedding tables at higher precision, and an already-4-bit release cannot be quantized upward at all. The KV cache is 2 · layers · kv_heads · head_dim · bytes per token, summed over layers with each sliding-window layer capped at its window. The speed figure is a roofline bound, not a benchmark: bandwidth divided by bytes read per token, which no runtime exceeds and every runtime falls short of. The usable fraction of card memory is an assumption: 92% here, which is what this lane assumes for a dedicated card — the other class assumes 75%, so it is not one number applied to every device. That is the only assumed input on this page; every other figure is computed from the model config and the card's published specification. Nothing on this page is written by a language model. Architecture from the model's published config, fetched 2026-08-07.