01 // Inference benchmarks
Single-stream decode · llama.cpp
# env llama.cpp b4732 · 4096 ctx · batch=1 · prompt=512 · temp=0.0 · median of 5 runs
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
ArchitectureAmpere
Process nodeSamsung 8N
Memory48 GB
Memory bandwidth768 GB/s
FP16 compute38.7 TFLOPS
INT8 compute77 TOPS
TDP300 W
PCIeGen 4 x16
Form factorDual-slot
CoolingBlower
03 // Model fit
Approximate VRAM required to load weights + 4096 ctx KV cache.
+ STRENGTHS
- ✓48GB VRAM is enough for 70B-class models at Q8
- ✓768 GB/s memory bandwidth · top tier in its class
- ✓Strong tooling: FP16, Q8, Q4 all officially supported
− TRADE-OFFS
- −Draws 300W under load — plan PSU and thermals accordingly
- −Limited to dual-slot chassis
- −Driver lock-in to vendor stack
related research
Research behind NVIDIA RTX A6000 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