Home / Models / Qwen3 8B / NVIDIA RTX 3090 Ti 24 GB
Can Qwen3 8B run on NVIDIA RTX 3090 Ti 24 GB?
Yes. Calculated memory fit: Qwen3 8B 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
- Qwen3 8B · 8.2B
- GPU
- NVIDIA RTX 3090 Ti 24 GB · 24 GB advertised
What else can NVIDIA RTX 3090 Ti 24 GB run? · Find other GPUs for Qwen3 8B
Memory calculation
Breakdown for Q4 at 8K context, batch size 1. Calculator v1.0.0.
| Model weights | 4.8 GB |
|---|---|
| KV cache | 1.1 GB |
| Runtime reserve | 0.7 GB |
| Safety margin | 0.4 GB |
| Required (estimated) | 7.1 GB |
| GPU usable VRAM | 21.6 GB |
Result: FITS IN VRAM
Quantization table
| Quantization | 2K | 4K | 8K | 16K | 32K | 40K |
|---|---|---|---|---|---|---|
| BF16 | Fits | Fits | Fits | Fits | Limited context | Limited context |
| FP16 | Fits | Fits | Fits | Fits | Limited context | Limited context |
| Q3 | Fits | Fits | Fits | Fits | Fits | Fits |
| Q4 | Fits | Fits | Fits | Fits | Fits | Fits |
| Q5 | Fits | Fits | Fits | Fits | Fits | Fits |
| Q6 | Fits | Fits | Fits | Fits | Fits | Fits |
| Q8 | Fits | Fits | Fits | Fits | Fits | Fits |
What fits entirely in VRAM?
- BF16 at 16K context
- BF16 at 2K context
- BF16 at 4K context
- BF16 at 8K context
- FP16 at 16K context
- FP16 at 2K context
- FP16 at 4K context
- FP16 at 8K context
- Q3 at 16K context
- Q3 at 2K context
- Q3 at 32K context
- Q3 at 4K 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
- Qwen3 8B
- GPU
- NVIDIA RTX 3090 Ti 24 GB
- Runtime
- llama.cpp · CUDA · model 1.0.0
- Decode
- ~128 tok/s · Calibrated estimate · HIGH confidence (spread ~112 tok/s–~143 tok/s)
- Prefill
- ~5698 tok/s at a 512-token prompt · Calibrated estimate · MEDIUM confidence
- Time to first token
- ~90 ms for a 512-token prompt (512 / prefill tok/s). Not estimated at 8K–128K.
decode_tps = GPU_efficiency × memory_bandwidth / model_weight_bytes; efficiency from same-gpu Llama 2 7B Q4_0 efficiency
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.
MSI SUPRIM GeForce RTX 3090 Ti X 24G NVIDIA 24 GB GDDR6X
24 GB · Amazon EXACT
Check price on Amazon
ASUS TUF Gaming TUF-RTX3090TI-24G-GAMING NVIDIA GeForce RTX 3090 Ti 24 GB GDDR6X
24 GB · Amazon EXACT
Check price on Amazon
MSI GAMING GeForce RTX 3090 Ti X TRIO 24GB NVIDIA GDDR6X
24 GB · Amazon unmatched
ASUS TUF Gaming TUF-RTX3090TI-O24G-GAMING NVIDIA GeForce RTX 3090 Ti 24 GB GDDR6X
24 GB · Amazon unmatched
ASUS ROG -STRIX-LC-RTX3090TI-O24G-GAMING NVIDIA GeForce RTX 3090 Ti 24 GB GDDR6X
24 GB · Amazon unmatched
Sources
Model: official config Qwen/Qwen3-8B (main). GPU/product specs: Icecat. Compatibility: RigForAI calculator v1.0.0 estimated VRAM · Q8 at 8K: FITS_IN_VRAM. Amazon: affiliate commerce match, not a spec source.