Gemma 3 27B IT
Data checked: 2026-10-12. Review interval shown below. Prices need review after 30 days; specifications and benchmark collections after 90 days. Dates describe the recorded check, not a live retailer quote.
- Verified Checked against the primary source (for example the model’s config.json on Hugging Face).
- Sourced Taken from a cited source; not yet independently checked.
- Estimate Calculated with the published formulas; not a measurement.
- Sample Placeholder data for layout; not a real figure.
- Not yet measured No public, citable score found for this benchmark yet. We never fill the gap with a guess.
Verified metadata is not a measured performance result. Only cited runs matching model, device, quant and recorded context can replace speed estimates. Estimates assume one stream and F16 KV cache, with a ±25% uncertainty band. A published run applies only to its recorded runtime and conditions; missing settings are unknown.
Evidence labels · Freshness policy · Calculation methodology
At a glance
The 24 GB-card workhorse. Five of every six layers only look at the last 1,024 tokens, so the KV cache stays small as context grows.
At Q4_K_M and 8k context it needs about 18.2 GB: 16.6 GB of weights, 1.1 GB of KV cache and 0.6 GB of runtime overhead. It is dense, so every token reads all 16.6 GB of weights; speed scales with memory bandwidth. 52 of its 62 layers use a 1,024-token sliding window, which keeps the cache small at long context.
Estimated setup
Quant | Q4_K_M |
|---|---|
Smallest standard card size at 8k | 24 GB |
Max context, 24 GB card | 64k |
Runtimes | llama.cpp, Ollama, LM Studio, MLX |
Quantization options
Total memory = weights + KV cache (F16) + runtime overhead. File sizes marked Verified are the sum of GGUF files from bartowski/google_gemma-3-27b-it-GGUF on the Hugging Face API. KV cache costs 0.508 MB per token before sliding-window savings.
Quant | bpw | File | At 8k | At 32k | At 128k | KL-divergence | 24 GB card, 8k |
|---|---|---|---|---|---|---|---|
| Q3_K_M | 3.91 | 13.4 GBVerified | 15.1 GB | 17.3 GB | 26.1 GB | 0.071Sample | Fits |
| Q4_K_M | 4.85 | 16.6 GBVerified | 18.2 GB | 20.4 GB | 29.2 GB | 0.017Sample | Fits |
| Q5_K_M | 5.69 | 19.3 GBVerified | 20.9 GB | 23.1 GB | 31.9 GB | 0.008Sample | Fits |
| Q6_K | 6.56 | 22.2 GBVerified | 23.8 GB | 26.0 GB | 34.8 GB | 0.004Sample | Needs offload |
| Q8_0 | 8.50 | 28.7 GBVerified | 30.4 GB | 32.6 GB | 41.4 GB | 0.001Sample | Needs offload |
| F16 | 16.00 | 54.0 GBVerified | 55.7 GB | 57.9 GB | 66.7 GB | reference | Needs offload |
Why this matters: What is quantization? · What is a KV cache?
Decode speed by device
Q4_K_M, single stream, 8k context. Estimates use the brief §7.3 formula (η × bandwidth ÷ bytes per token) and are ±25% until measured.
Speed table
Device | Verdict | Decode, tok/s | Range | Runtime |
|---|---|---|---|---|
| GeForce RTX 5090 32 GB | Fits | 60.9Estimate | ±25% | llama.cpp (estimate) |
| GeForce RTX 4090 24 GB | Fits | 34.3Estimate | ±25% | llama.cpp (estimate) |
| Radeon RX 7900 XTX 24 GB | Fits | 32.6Estimate | ±25% | llama.cpp (estimate) |
| GeForce RTX 3090 24 GB (used) | Fits | 31.8Estimate | ±25% | llama.cpp (estimate) |
| 2× RTX 3090 (used) 48 GB | Fits | 31.8Estimate | ±25% | llama.cpp (estimate) |
| Mac Studio M5 Ultra (base) | Fits | 30.6Estimate | ±25% | llama.cpp (estimate) |
| Mac Studio M5 Ultra · 512 GB | Fits | 30.6Estimate | ±25% | llama.cpp (estimate) |
| MacBook Pro M4 Max 40c · 128 GB | Fits | 13.9Estimate | ±25% | llama.cpp (estimate) |
| Mac Studio M5 Max (base) | Fits | 11.7Estimate | ±25% | llama.cpp (estimate) |
| GeForce RTX 5060 Ti 16 GB | Needs offload | 7.1Estimate | ±25% | llama.cpp (estimate) |
| NVIDIA DGX Spark FE 128 GB | Fits | 7.0Estimate | ±25% | llama.cpp (estimate) |
| Ryzen AI Max+ 395 mini PC 128 GB | Fits | 6.5Estimate | ±25% | llama.cpp (estimate) |
| GeForce RTX 4060 Ti 16 GB | Needs offload | 5.8Estimate | ±25% | llama.cpp (estimate) |
| CPU only · Ryzen 9 9950X + 128 GB DDR5 | Fits | 2.5Estimate | ±25% | llama.cpp (estimate) |
Compare with similar-sized models
Model | Parameters | Quality score | Licence |
|---|---|---|---|
| Gemma 3 27B IT | 27.4 B | 1,358Sourced | Gemma Terms of Use |
| Qwen3 14B | 14.8 B | Not measuredSample | Apache-2.0 |
| gpt-oss-20b | 20.9 B | 1,287Sourced | Apache-2.0 |
LMArena text, overall, entry 'gemma-3-27b-it': 1357.7 (95% CI 1354.1–1361.4, 46,672 votes), leaderboard published 2026-10-08. CC-BY-4.0. Dataset
llama-perplexity --kl-divergence on a fixed corpus. Speed and quality are never merged into one score. Sample values are illustrative and must not be used to select a model.Architecture
Parameters, total | 27.43 BVerifiedsafetensors.total from the HF API. Read from an ungated mirror of the gated official repo; identical architecture. Source |
|---|---|
Parameters, active per token | 27.43 BVerifiedDense: all parameters active. Source |
Layers | 62VerifiedRead from an ungated mirror of the gated official repo; identical architecture. Source |
Query heads | 32VerifiedRead from an ungated mirror of the gated official repo; identical architecture. Source |
KV heads (GQA) | 16VerifiedRead from an ungated mirror of the gated official repo; identical architecture. Source |
Head dimension | 128VerifiedRead from an ungated mirror of the gated official repo; identical architecture. Source |
Hidden size | 5376VerifiedRead from an ungated mirror of the gated official repo; identical architecture. Source |
Sliding window | 1,024 tokensVerifiedLocal attention window. Source |
Sliding-window layers | 52 of 62VerifiedLayers using sliding-window attention (from layer_types / sliding_window_pattern). Source |
Operating conditions
Max context | 131,072 tokensVerifiedSource |
|---|---|
Licence | Gemma Terms of UseVerifiedSource |
Weights | google/gemma-3-27b-it |
GGUF files | bartowski/google_gemma-3-27b-it-GGUF |
Usable memory, discrete | VRAM − 0.5 GB2 |
Usable memory, unified | RAM × 0.752 |
Fits when | total ≤ 90% of usable2 |
1 Estimates: decode ≈ η × bandwidth ÷ (active weights + KV cache), η 0.60 discrete / 0.45 unified / 0.50 CPU (concept brief §7.3). Not measurements.
2 Fit rules from the concept brief §7.3, including the 0.75 unified-memory factor used by canirun.ai.
Flags: Verified = checked against config.json / HF API; Sourced = from a cited source; Estimate = formula; Sample = placeholder. Markdown · JSON · Report a problem