Short answer: RTX 4070 SUPER is a 12GB Ada GPU. It provides a useful local-AI tier for many 7B and 8B models and some quantized 14B artifacts, but 12GB is not a promise that every 14B workload will remain resident. Model file, KV cache, runtime workspace, display allocation, modality and concurrency share the same capacity.
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 SUPER specifications that matter
| Official reference specification | RTX 4070 SUPER | Local-AI planning meaning |
|---|---|---|
| Architecture | Ada Lovelace | CUDA capability 8.9; verify runtime and extension support |
| Memory | 12GB GDDR6X | Weights, cache, workspace, display use and concurrency share this capacity |
| Memory interface | 192-bit | Interface width is not an application-speed result |
| CUDA cores | 7,168 | Core count does not establish local-LLM throughput |
| Reference boost clock | 2.48GHz | Partner boards, power limits and sustained clocks vary |
| CUDA capability | 8.9 | Compiled kernels and extensions must support the exact card |
| Total graphics power | 220W | Measure complete-workload energy and cooling |
| Required system power | 650W reference | NVIDIA notes that actual needs depend on system configuration |
| Founders Edition size | 244 × 112mm; 2-slot | Partner boards vary; verify case, cable and airflow clearance |
| NVLink | No | Multiple GPUs remain separate devices and require explicit placement |
Exact 7B and 14B file boundaries on 12GB
The following values come from Qwen’s official Qwen2.5-7B-Instruct-GGUF and Qwen2.5-14B-Instruct-GGUF repositories. Margins subtract displayed file size from nominal 12GB capacity only; they exclude KV cache, runtime workspace, display use, modality and concurrency.
| Official artifact | Displayed size | 12GB file-only margin | Planning result |
|---|---|---|---|
| Qwen2.5-7B Q4_K_M | 4.68GB | 7.32GB | Useful starting point; check full allocation |
| Qwen2.5-7B Q6_K | 6.25GB | 5.75GB | Useful headroom before cache and workspace |
| Qwen2.5-7B Q8_0 | 8.1GB | 3.9GB | Bounded headroom; calculate context and runtime overhead |
| Qwen2.5-14B Q4_K_M | 8.99GB | 3.01GB | Capacity-sensitive; check the complete run |
| Qwen2.5-14B Q6_K | 12.1GB | Does not fit wholly | Requires offload, another placement or more device memory |
| Qwen2.5-14B Q8_0 | 15.7GB | Does not fit wholly | Move to another artifact or capacity tier |
The official 7B files leave 3.9GB to 7.32GB of nominal room in these examples. The 14B Q4 file leaves 3.01GB, while Q6 and Q8 exceed 12GB before runtime allocations. Read the 7B/8B VRAM guide and 14B VRAM guide.
When RTX 4070 SUPER is a sensible tier
- 7B and 8B text models with explicit cache and context limits.
- Selected 14B quantized artifacts after the complete allocation is checked.
- Image, speech and embedding workloads whose measured peak remains below 12GB.
- A single-user workstation where CUDA support matters more than larger model capacity.
When to move beyond 12GB
- The desired model file alone approaches or exceeds 12GB.
- Long context, vision inputs, higher cache precision or concurrency erase the file-only margin.
- CPU offload makes the waiting time or throughput unacceptable.
- A 16GB or 24GB card changes the feasible workload rather than merely speed.
Compare the 12GB GPU buyer guide, then use the 8GB/12GB/16GB capacity guide when the complete run needs another tier.
Partner-board and used checks
Confirm the exact model and 12GB capacity in the runtime. Partner boards may change dimensions, slot width, connector routing, cooling, clocks and power limits. Check 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. Gaming rankings, AI TOPS and one synthetic score are not local-LLM results.
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 RTX 4070 SUPER user guide
- NVIDIA CUDA GPU compute-capability table
- Official Qwen2.5-7B-Instruct-GGUF repository
- Official Qwen2.5-14B-Instruct-GGUF repository
- HighVRAM methodology
Reviewed October 25, 2026. HighVRAM has not independently benchmarked RTX 4070 SUPER. Capacity, core count, clocks, architecture and peak AI metrics do not establish application speed, output quality or value.