Short answer: RTX 4070 Ti SUPER is a 16GB Ada GPU. It can be a practical single-card local-AI option when the complete model, KV cache, runtime workspace, display allocation and concurrency fit inside the device. Its 16GB capacity is the same planning tier as other 16GB cards; platform specifications do not by themselves prove a universal speed or value result.
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 4070 Ti SUPER specifications that matter
| Official reference specification | RTX 4070 Ti 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 an application-speed result |
| CUDA cores | 8,448 | Core count does not establish local-LLM throughput |
| Reference boost clock | 2.61GHz | Partner boards, power limits and sustained clocks vary |
| CUDA capability | 8.9 | Compiled kernels and extensions must support the exact card |
| Total graphics power | 285W | Measure complete-workload energy and cooling |
| Required system power | 700W reference | NVIDIA notes that actual needs depend on system configuration |
| Board size | Varies by manufacturer | Verify case, slot, cable and airflow clearance for the exact board |
| NVLink | No | 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 4070 Ti SUPER workloads
- 7B and 8B text models with room for context and runtime allocations.
- Many 14B quantized paths after the complete 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.
Partner-board and used checks
Confirm the exact model and 16GB capacity in the runtime. Board dimensions vary by manufacturer. Check connector routing, slot width, case clearance, cooling, warranty and return protection. For used cards, inspect repair history, fan behavior, memory stability and hotspot-to-core temperature delta with 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 4070 family official specifications
- 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. HighVRAM has not independently benchmarked RTX 4070 Ti SUPER. Capacity, core count, clocks, architecture and peak AI metrics do not establish application speed, output quality or value.