Home / Models / Gemma 2 27B Instruct / NVIDIA RTX 5090 32 GB
Can Gemma 2 27B Instruct run on NVIDIA RTX 5090 32 GB?
Yes. Calculated memory fit: Gemma 2 27B Instruct can run entirely in this GPU’s usable VRAM at one or more supported quantizations.
Can it run?
- Can it run?
- Yes, full GPU fit (calculated estimate).
- Full GPU fit?
- Yes
- Model
- Gemma 2 27B Instruct · 27B
- GPU
- NVIDIA RTX 5090 32 GB · 32 GB advertised
What else can NVIDIA RTX 5090 32 GB run? · Find other GPUs for Gemma 2 27B Instruct
Memory calculation
Breakdown for Q4 at 8K context, batch size 1. Calculator v1.0.0.
| Model weights | 16.0 GB |
|---|---|
| KV cache | 2.9 GB |
| Runtime reserve | 1.3 GB |
| Safety margin | 0.7 GB |
| Required (estimated) | 20.9 GB |
| GPU usable VRAM | 28.8 GB |
Result: FITS IN VRAM
Quantization table
| Quantization | 2K | 4K | 8K |
|---|---|---|---|
| BF16 | Offload | Offload | Offload |
| FP16 | Offload | Offload | Offload |
| Q3 | Fits | Fits | Fits |
| Q4 | Fits | Fits | Fits |
| Q5 | Fits | Fits | Fits |
| Q6 | Fits | Fits | Fits |
| Q8 | Offload | Offload | Offload |
What fits entirely in VRAM?
- Q3 at 2K context
- Q3 at 4K context
- Q3 at 8K context
- Q4 at 2K context
- Q4 at 4K context
- Q4 at 8K context
- Q5 at 2K context
- Q5 at 4K context
- Q5 at 8K context
- Q6 at 2K context
- Q6 at 4K context
- Q6 at 8K context
What requires RAM offload?
Offload is shown only when the model does not fully fit in usable VRAM but stays within the modeled offload allowance.
Performance
Separate from memory fit. Runtime is llama.cpp · quantization bucket Q4 (GGUF Q4_0 maps to Q4). Workload is llama-bench pp512 / tg128 (short context, batch 1, full GPU offload). This is not a ranking and not a 8K–128K speed claim.
- Model
- Gemma 2 27B Instruct
- GPU
- NVIDIA RTX 5090 32 GB
- Runtime
- llama.cpp · CUDA · model 1.0.0
- Decode
- ~67 tok/s · Estimated · LOW confidence (spread ~40 tok/s–~94 tok/s)
- Prefill
- unavailable for this workload
- Time to first token
- unavailable
decode_tps = GPU_efficiency × memory_bandwidth / model_weight_bytes; efficiency from same-gpu Llama 2 7B Q4_0 efficiency; param_ratio=4.04
Available graphics cards
These SKUs use this GPU chip. Amazon CTAs appear only for EXACT/HIGH matches. Absence of a price does not change the compatibility result above.
ASUS TUF Gaming TUF-RTX5090-O32G-GAMING NVIDIA GeForce RTX 5090 32 GB GDDR7
32 GB · Amazon EXACT
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ASUS ROG Astral - -LC-RTX5090-O32G-GAMING NVIDIA GeForce RTX 5090 32 GB GDDR7
32 GB · Amazon EXACT
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GIGABYTE GeForce RTX 5090 WINDFORCE OC 32G Graphics Card
32 GB · Amazon EXACT
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GIGABYTE AORUS GeForce RTX 5090 MASTER 32G Graphics Card - 32GB GDDR7, 512bit, PCI-E 5.0, 2655MHz Core Clock, 3 x DP 2.1a, 1 x HDMI 2.1b, GV-N5090AORUS M-32GD
32 GB · Amazon EXACT
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ASUS ROG Astral - -RTX5090-O32G-GAMING NVIDIA GeForce RTX 5090 32 GB GDDR7
32 GB · Amazon EXACT
Check price on AmazonASUS TUF Gaming TUF-RTX5090-32G-GAMING NVIDIA GeForce RTX 5090 32 GB GDDR7
32 GB · Amazon EXACT
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Sources
Model: official config google/gemma-2-27b-it (official-fallback-config). GPU/product specs: Icecat. Compatibility: RigForAI calculator v1.0.0 estimated VRAM · Q8 at 8K: CPU_RAM_OFFLOAD_REQUIRED. Amazon: affiliate commerce match, not a spec source.