Comparing 16GB Ada boards? Pair this guide with the RTX 4070 Ti SUPER 16GB profile; hold workload settings constant before inferring speed or value.
Short answer: RTX 4080 SUPER is a 16GB Ada GPU that can be a strong local-AI option when the complete model, cache, runtime workspace and concurrency fit comfortably inside its device memory. It is not a 24GB-class card: exact 14B artifacts can range from comfortable to unsafe at 16GB, while common 32B Q4 files exceed the device before runtime allocations.
Check the complete allocation: enter the exact artifact, context and concurrency in the LLM VRAM calculator. File size alone is not peak resident memory.
Official RTX 4080 SUPER specifications that matter
| Official reference specification | RTX 4080 SUPER | Local-AI planning meaning |
|---|---|---|
| Architecture | Ada Lovelace | CUDA capability 8.9; verify runtime and extension support |
| Memory | 16GB GDDR6X | Weights, cache, workspace, display use and concurrency share this capacity |
| Memory interface | 256-bit | Interface width is not a standalone token-rate result |
| CUDA cores | 10,240 | Core count does not provide an application-speed result |
| Reference boost clock | 2.55GHz | Partner boards, power limits and sustained clocks vary |
| CUDA capability | 8.9 | Compiled kernels and extensions must support the exact card |
| Total graphics power | 320W | Measure complete-workload energy and cooling |
| Required system power | 750W reference | NVIDIA notes that actual needs depend on the system configuration |
| Founders Edition size | 304 × 137mm; 3-slot specification | Partner boards vary; verify case, cable and airflow clearance |
| Multi-GPU memory | No automatic pooled-memory path | Multiple GPUs remain separate devices and require explicit placement |
Exact 14B and 32B file boundaries on 16GB
The following values come from Qwen’s official Qwen2.5-14B-Instruct-GGUF and Qwen2.5-32B-Instruct-GGUF repositories. Margins are file-size subtraction from nominal 16GB capacity only; they exclude KV cache, runtime workspace, display use, modality and concurrency.
| Official artifact | Displayed size | 16GB file-only margin | Planning result |
|---|---|---|---|
| Qwen2.5-14B Q4_K_M | 8.99GB | 7.01GB | Useful starting point; check full allocation |
| Qwen2.5-14B Q6_K | 12.1GB | 3.9GB | Bounded headroom before cache and workspace |
| Qwen2.5-14B Q8_0 | 15.7GB | 0.3GB | Not a safe resident target |
| Qwen2.5-32B Q2_K | 12.3GB | 3.7GB | Quality and complete allocation require testing |
| Qwen2.5-32B Q3_K_M | 15.9GB | 0.1GB | Not a safe resident target |
| Qwen2.5-32B Q4_K_M | 19.9GB | Does not fit wholly | Requires offload, another placement or more device memory |
The 14B Q4 file leaves 7.01GB of nominal room; Q6 leaves 3.9GB; Q8 leaves only 0.3GB and is not a safe target. For the 32B example, even Q3 leaves only 0.1GB and Q4 exceeds the card. Read the 14B VRAM guide and 32B VRAM guide.
Good RTX 4080 SUPER workloads
- 7B and 8B text models with substantial room for context and runtime allocations.
- Many 14B quantized paths after the full allocation is checked.
- Image, speech and embedding workloads whose measured peak stays below 16GB.
- Single-user or controlled-concurrency services with explicit cache limits.
When 16GB is the wrong tier
- The desired artifact alone is close to 16GB.
- Long context, higher cache precision, vision inputs or concurrency erase the file-only margin.
- CPU offload would make latency or throughput unacceptable.
- A 24GB or 32GB card changes the workload rather than merely increasing speed.
Compare the broader 16GB GPU buyer guide, then use the 24GB/32GB/48GB capacity guide when the complete run outgrows this tier.
Used and partner-board checks
Confirm the exact model and 16GB capacity in the runtime. For used cards, inspect repair history, connector and cable condition, fan behavior, memory stability, hotspot-to-core temperature delta, warranty and return protection. Partner boards may change dimensions, connector routing, cooling, clocks and power limits. Use the used-GPU checklist.
Measure instead of inferring
A reproducible result records exact board, driver, runtime and version, model revision, quantization, context, cache precision, modality, prompt/output profile, batch, concurrency, timing definition, repetitions, exposed memory, peak device memory, CPU offload, system RAM, power limit and wall power. Do not use gaming rankings, AI TOPS or one synthetic score as a local-LLM result.
Choose the next evidence
Run a controlled workload · Submit a reproducible result · Stage a private watchlist · Check screened observations
Sources
- NVIDIA GeForce RTX 4080 SUPER official specifications
- NVIDIA GeForce RTX 4080 SUPER user guide
- NVIDIA CUDA GPU compute-capability table
- Official Qwen2.5-14B-Instruct-GGUF repository
- Official Qwen2.5-32B-Instruct-GGUF repository
- HighVRAM methodology
Reviewed October 25, 2026. Manufacturer specifications and official model repositories are linked. HighVRAM has not independently benchmarked RTX 4080 SUPER. Capacity, core count, clocks, architecture and peak AI metrics do not establish application speed, output quality or value.
Choosing between 24GB Radeon capacity and a 16GB Ada CUDA path? Use the RX 7900 XTX vs RTX 4080 SUPER local-AI comparison for exact 32B file margins, software checks and matched benchmark controls.