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
ArchitectureAmpere
Process nodeSamsung 8N
Memory12 GB
Memory bandwidth360 GB/s
FP16 compute12.7 TFLOPS
INT8 compute25 TOPS
TDP170 W
PCIeGen 4 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
- ✓12GB clears our Qwen3 14B Q4 planning target
- ✓360 GB/s memory bandwidth · top tier in its class
- ✓Indexed formats: FP16, Q8, Q4 · verify support in your runtime
− TRADE-OFFS
- −Draws 170W under load — plan PSU and thermals accordingly
- −Limited to dual-slot chassis
- −Driver lock-in to vendor stack
related research
Research behind GeForce RTX 3060 12GB 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