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
ArchitectureBlackwell
Process nodeTSMC 4NP
Memory16 GB
Memory bandwidth960 GB/s
FP16 compute56.3 TFLOPS
INT8 compute112 TOPS
TDP360 W
PCIeGen 5 x16
Form factorDual-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
- ✓16GB clears our Codestral 22B Q4 planning target
- ✓960 GB/s memory bandwidth · top tier in its class
- ✓Indexed formats: FP16, FP8, Q8, Q4 · verify support in your runtime
− TRADE-OFFS
- −Draws 360W under load — plan PSU and thermals accordingly
- −Limited to dual-slot chassis
- −Driver lock-in to vendor stack
related research
Research behind GeForce RTX 5080 inference tradeoffs
These papers explain the quantization, cache, bandwidth, and runtime constraints that matter before buying this GPU for local AI.
LLM quantization research
GPTQ, AWQ, GGUF, FP4, NF4, and what low-bit formats mean for VRAM fit.
Open GPU inference optimization papers
Memory bandwidth, FlashAttention, dequant kernels, and backend maturity.
Open 2026 LLM inference papers
Fresh 2026 work on FP4, KV cache, kernels, AMD serving, and local controllers.
Open