01 // Representative inference estimates
Single-stream decode · llama.cpp
Aggregated from published community sources. Test setups differ, so use close results as directional evidence.
Sources and normalization →01b // Performance across quantization
vs. nearest competitors
How tok/s scales from FP16 → Q8 → Q4 compared to GPUs in a similar price/VRAM range.
02 // Hardware specs
ArchitectureAda Lovelace
Process nodeTSMC 4N
Memory24 GB
Memory bandwidth1,008 GB/s
FP16 compute82 TFLOPS
INT8 compute165 TOPS
TDP450 W
PCIeGen 4 x16
Form factorTriple-slot
CoolingAxial
03 // Model fit
Weight estimate plus 15% planning headroom for runtime buffers and a modest KV cache. Long context can require more.
+ STRENGTHS
- ✓24GB clears our Qwen3 32B Q4 planning target
- ✓1008 GB/s memory bandwidth · top tier in its class
- ✓Indexed formats: FP16, FP8, Q8, Q4 · verify support in your runtime
− TRADE-OFFS
- −Draws 450W under load — plan PSU and thermals accordingly
- −Limited to triple-slot chassis
- −Driver lock-in to vendor stack
related research
Research behind GeForce RTX 4090 inference tradeoffs
These papers explain the quantization, cache, bandwidth, and runtime constraints that matter before buying this GPU for local AI.
Local AI inference papers
llama.cpp, Apple Silicon, constrained GPUs, offload, and one-box inference.
Open LLM quantization research
GPTQ, AWQ, GGUF, FP4, NF4, and what low-bit formats mean for VRAM fit.
Open KV cache optimization papers
Cache quantization, compression, reuse, and long-context memory pressure.
Open