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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsShort answer: Nemotron 3.5 Super VL is a compelling choice when an agent must combine OCR-heavy documents, images, video, long context and tool calls, particularly on NVIDIA infrastructure. Qwen2.5-VL remains the better-documented alternative for document, chart, video and visual-computer-use workloads. There is no independent, controlled benchmark proving that one model is universally better.
The practical decision depends on your visual inputs, agent harness, context length, GPU budget and serving stack. NVIDIA’s published speed and agent scores are promising, but they are vendor or setup-specific results rather than guarantees on different hardware.
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The decision in one view
Choose Nemotron 3.5 Super VL when you need one model to interpret text, images, video and complex documents while carrying a long-running tool-using workflow. Its 120-billion-parameter mixture-of-experts design activates about 12 billion parameters per token, and NVIDIA has released BF16 and NVFP4 checkpoints with recipes for vLLM, SGLang, TensorRT-LLM and NVIDIA Dynamo.
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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteChoose Qwen2.5-VL when you want a clearly documented open vision-language family available in 7B, 32B and 72B sizes, with technical-report coverage of dynamic-resolution images, invoices, forms, charts, diagrams, long videos and visual interaction with computers or phones. Smaller variants can be easier to fit into a limited GPU budget.
#1 Best Overall
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Do not treat either model’s headline score as a purchase guarantee. Measure the exact combination of documents, video, tools, concurrency, context length, quantization and hardware that your application will use.
What Nemotron 3.5 Super VL actually is
NVIDIA describes Nemotron 3.5 Super as an open 120B-total/12B-active mixture-of-experts model that combines Mamba and Transformer components with vision-language inputs. The model is initialized from a text-only Nemotron 3 Super base. Supervised fine-tuning, reinforcement learning from verifiable rewards, specialist teachers and two phases of multi-teacher on-policy distillation are used to create the multimodal agent checkpoint.
The cookbook identifies a C-RADIO v4-H vision encoder. The resulting model accepts text, images and video; it is intended for document understanding and visual reasoning, and it is trained for contexts of up to 1 million tokens. That figure is a context target, not a promise that every serving configuration will sustain a million-token request at useful latency or fit it in a single accelerator.
The Tool Desk
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Why the mixture-of-experts design matters
Only a subset of the network is activated for each token, which reduces arithmetic compared with a dense 120B model. It does not normally reduce the amount of model data that must be resident: serving systems generally load the experts and route tokens among them. Memory is therefore driven mainly by total parameters, precision, framework overhead and cache size, while speed is influenced by the 12B active path, routing, batching and communication between GPUs.
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- Professional Intel Arc Pro B70 GPU: Built on the Intel Xe2-HPG architecture, it features 32 Xe cores and 256 XMX engines, designed to accelerate AI, rendering, and complex visualization workloads.
- Massive 32GB GDDR6 VRAM: Equipped with 32GB of high-speed GDDR6 memory on a 256-bit bus, running at 19 Gbps, which allows for handling large AI models and complex datasets locally.
- High-Performance Engine Clock: Delivers an engine clock of 2540 MHz, providing the compute power needed for demanding professional applications and AI inference.
Where Nemotron 3.5 Super VL is designed to excel
Visual documents, not just captions
NVIDIA’s stated focus is the information agents encounter in real work: OCR-heavy pages, tables, charts and structured information embedded in visual documents. That makes the model relevant to PDF extraction, web research, invoice or receipt processing, form interpretation and chart-based questions. The model is intended to preserve those observations while an agent continues reasoning and calling tools.
Long-running multimodal agents
A long context can hold earlier screenshots, extracted pages, tool results and intermediate decisions without repeatedly summarizing them. In a research or coding workflow, that can reduce the need to discard evidence between turns. In practice, cache reuse and the serving engine’s handling of image and video tokens matter as much as the advertised context limit.
Video understanding
Video is a first-class input modality for Nemotron 3.5 Super VL. NVIDIA lists video understanding among its target workflows, but the published material does not establish a universal frame rate, maximum duration or event-localization accuracy. Test those properties with your own footage and sampling policy rather than assuming that a long text context automatically means long, inexpensive video analysis.
Agent tool use and software work
NVIDIA positions the model for coding, web research, document intelligence, cybersecurity and other workflows in which the model must plan, call tools, inspect results and recover from errors. The strongest evidence is the published agent benchmark and serving data below; your harness, tool schemas and permission boundaries can change the outcome substantially.
Published performance: what the numbers do and do not prove
The following results are reported by NVIDIA. They are useful reference points, but they are not an independent head-to-head evaluation under a single reproducible test setup.
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| Measure | Reported result | How to interpret it |
|---|---|---|
| SWE-bench Pro | 51.78% | NVIDIA’s 2026 report; the harness, model settings and execution policy determine what this score represents. |
| Terminal-Bench 2.1 | 71.9% | Reported after parallel agent execution and self-verification, so it is not a simple single-pass language-model score. |
| Agentic serving throughput | 4,856 output tokens per second per GPU | Measured on one GB200 in NVIDIA’s evaluated 20-turn agentic workload. It is not a consumer-GPU expectation. |
| Relative throughput | 2.34× Qwen3.5 122B-A10B; 1.90× Qwen3.8 Flash-Next | NVIDIA’s stated interactive-serving conditions; hardware, quantization, batching and context must match before drawing a comparison. |
| Artificial Analysis agentic endpoint | More than 600 output tokens per second | NVIDIA’s technical-blog figure for that endpoint, not a guaranteed rate for self-hosting. |
| Hermes Bench | 63.44% versus 56.33% for Nemotron 3 Ultra | NVIDIA’s comparison in its technical blog; it shows an internal-generation difference, not a Qwen2.5-VL comparison. |
| LangChain evaluations | 87.05% versus 84.3% for Nemotron 3 Ultra | Again, an NVIDIA-reported comparison with the earlier Nemotron model. |
These figures answer different questions. SWE-bench Pro and Terminal-Bench 2.1 indicate performance in software or terminal environments under specified agent procedures. Tokens per second describes serving speed in a particular system. None directly measures OCR character accuracy, table-cell fidelity, chart reasoning or video event localization.
How Qwen2.5-VL compares
Qwen2.5-VL is the clearest open comparator documented for the same broad class of work. Its technical report describes dynamic-resolution vision processing, precise object localization, invoice, form and table extraction, chart and diagram analysis, long-video comprehension with second-level event localization, and visual tool use for computer or mobile interaction.
| Qwen2.5-VL variant | Positioning | Practical implication |
|---|---|---|
| 7B | Smallest published family member | More approachable for constrained deployments, although quality and speed must be measured on your documents and tools. |
| 32B | Mid-size model | A compromise between capability and memory for teams that cannot host a 72B or 120B-total model. |
| 72B | Largest published family member | The Qwen report says it matches state-of-the-art systems such as GPT-4o and Claude 3.5 Sonnet on the report’s stated tasks; those are report claims, not a controlled Nemotron comparison. |
Qwen’s report supplies more explicit descriptions of visual grounding and second-level video event localization than the available Nemotron material. Nemotron, in turn, supplies a stronger documented emphasis on integrated agent operation, long context and NVIDIA serving efficiency. That difference in documentation should not be mistaken for proof that one model has higher raw visual accuracy.
Nemotron 3.5 Super VL versus Qwen2.5-VL by workload
| Workload or requirement | More persuasive starting point | Reason and qualification |
|---|---|---|
| OCR-heavy PDFs and pages | Benchmark both; Nemotron has a direct stated focus | NVIDIA explicitly targets OCR, tables and structured visual documents. Qwen documents invoice, form and table extraction. Your scans, fonts and languages decide the winner. |
| Charts and diagrams | Benchmark both | Both sources discuss visual reasoning; Qwen’s report specifically names chart and diagram analysis, while Nemotron emphasizes charts in agent-facing documents. |
| Long video with event timestamps | Qwen2.5-VL as the initial test | Qwen’s report explicitly describes second-level event localization. Nemotron supports video, but the supplied material does not provide an equivalent localization result. |
| Computer or mobile visual interaction | Qwen2.5-VL as the initial test | Qwen’s report explicitly describes visual tool use for computer and mobile interaction. Validate action reliability rather than relying on a capability list. |
| Long multimodal agent sessions | Nemotron 3.5 Super VL as the initial test | Its 1M-token training target and agent-oriented post-training are designed for this pattern; sustained latency and cache behavior still require measurement. |
| Software-engineering agents | Nemotron for a first trial | NVIDIA publishes SWE-bench Pro and Terminal-Bench 2.1 results. Run the same repository, tools and verification policy on every candidate. |
| Small single-node deployment | Qwen2.5-VL 7B or 32B as the initial test | The smaller published sizes can reduce memory pressure. Nemotron’s total parameter count makes a multi-accelerator plan more realistic. |
| NVIDIA-centric high-throughput service | Nemotron 3.5 Super VL as the initial test | The published 4,856-token-per-second result and serving recipes target NVIDIA hardware. They do not establish the same advantage on other accelerators. |
Can you run Nemotron Super VL locally?
Yes, but “locally” should be understood as self-hosting on suitable accelerator hardware, not as a promise that a typical laptop can load the model. The total 120B parameter count dominates weight memory.
Raw weight-memory estimates
These are arithmetic estimates before runtime overhead:
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- 24GB GDDR6 on 192-Bit Bus: Massive 24GB memory with 456 GB/s bandwidth – ideal for LLMs, AI inference, 3D rendering, and generative design.
- Intel Xe2-HPG Architecture: Built on Intel's next-gen architecture with 20 Xe cores and 160 XMX engines for AI acceleration (197 INT8 TOPS).
- PCIe 5.0 Support: PCI Express 5.0 x16 interface for maximum bandwidth with the latest workstation platforms.
- BF16: 120 billion parameters × 2 bytes is about 240 GB in decimal units, or roughly 224 GiB.
- Four-bit storage: 120 billion parameters × 0.5 bytes is about 60 GB in decimal units, or roughly 56 GiB, before scale metadata and other quantization overhead.
- Active parameters: the approximately 12B active path reduces per-token computation, but it does not by itself make the full 120B checkpoint fit in 12B worth of memory.
Real deployments need additional space for quantization scales, the C-RADIO v4-H vision encoder, runtime buffers, routing, CUDA or framework allocations, and the key-value cache. A million-token context can require an especially large cache, and image or video tokens increase the effective sequence length. Plan for headroom rather than sizing a GPU to the raw arithmetic alone.
What the released precisions mean
The BF16 checkpoints favor numerical fidelity and broad framework compatibility but require substantial memory. The NVFP4 checkpoint is intended to reduce weight storage and improve serving efficiency on supported NVIDIA hardware; its actual quality, memory use and speed depend on the implementation. Quantization can alter OCR, spatial reasoning or tool-call reliability, so evaluate the exact checkpoint you plan to deploy.
Why a single GB200 result does not answer your hardware question
NVIDIA’s 4,856 output tokens per second per GPU was measured on one GB200 in a particular 20-turn workload. It says nothing definitive about a different GPU, a different interconnect, lower concurrency, a larger context, video inputs or a consumer workstation. The only safe way to estimate capacity is to run a representative trace and record latency, throughput, memory use and failure rates.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Deployment options and a practical setup path
NVIDIA provides recipes for vLLM, SGLang, TensorRT-LLM and NVIDIA Dynamo. Treat them as alternative serving paths rather than interchangeable performance guarantees.
- Select the checkpoint. Start with the post-trained BF16 checkpoint for a quality baseline, then test NVFP4 if memory or throughput is the limiting factor. Keep the base checkpoint for experiments that require your own post-training.
- Choose the serving engine. Use the recipe that matches your operational stack: vLLM or SGLang for general model serving, TensorRT-LLM for NVIDIA-optimized inference, or Dynamo for a distributed NVIDIA deployment. Confirm that the chosen version supports the checkpoint’s multimodal inputs and tool-call format.
- Plan memory before loading weights. Add the raw weight estimate, vision encoder, runtime headroom and the cache required by your real context length. Decide whether experts are sharded across GPUs and whether image or video preprocessing runs inside the same service.
- Define the agent contract. Specify tool schemas, allowed arguments, timeout behavior, retries and how screenshots or documents are returned. A model can appear capable in chat while failing when a tool requires exact JSON or a strict action sequence.
- Set a context policy. Do not enable the maximum context by default. Measure quality and latency at the lengths your application actually sends, and use summarization or retrieval when old evidence no longer needs to remain in full.
- Measure before production. Record time to first token, output tokens per second, end-to-end tool latency, peak memory, error recovery and cost per completed task at realistic concurrency.
How to run a fair comparison
A useful evaluation compares completed tasks, not isolated prompts. Keep the model, quantization and serving conditions visible in every result.
Best Value
- NVIDIA Ampere Architecture-based CUDA Cores - Double-speed processing for single-precision floating point (FP32) operations and improved power efficiency provide significant performance improvements for graphics and simulation workflows, such as complex 3D computer-aided design (CAD) and computer-aided engineering (CAE), on the desktop.
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- Third-Generation NVIDIA NVLink - Increased GPU-to-GPU interconnect bandwidth provides a single scalable memory to accelerate graphics and compute workloads and tackle larger datasets.
- 48 Gigabytes (GB) of GPU Memory - Ultra-fast GDDR6 memory, scalable up to 96 GB with NVLink, gives data scientists, engineers, and creative professionals the large memory necessary to work with massive datasets and workloads like data science and simulation.
Build a workload matrix
- Documents: include clean PDFs, skewed scans, multi-column pages, tables that span pages, charts and forms with handwriting or stamps if those occur in production.
- Video: vary duration, scene changes, text on screen and the frame-sampling policy. Score both the answer and the time location returned.
- Agents: include successful tool calls, malformed tool results, timeouts, permission denials and tasks that require several dependent actions.
- Software: use the same repository state, tests, shell permissions, network policy and maximum turns for every model.
Score the failure modes users notice
- OCR character and field accuracy, including whether table rows and columns stay aligned.
- Chart or diagram answers that cite the correct series, axis and units.
- Object localization or bounding-box precision when spatial grounding matters.
- Tool-call exactness, invalid-argument rate, recovery after an error and unnecessary tool calls.
- Long-context retention: whether the agent can retrieve an earlier page or screenshot without inventing details.
- Latency and throughput at the same batch size, concurrency, context length, GPU count and precision.
Report the conditions with every number
Record GPU model, number of GPUs, interconnect, checkpoint, quantization, software versions, prompt and image sizes, video sampling, context length, batch or concurrency, cache policy and whether the result was vendor-reported or independently reproduced. Without those fields, a tokens-per-second claim cannot be transferred to another deployment.
Customization and production economics
NVIDIA cites two customer-specific examples. Scale AI reported 40-times lower inference cost and five-times lower latency after post-training Nemotron for vendor-credit processing. Ramp reported expectations of up to 50% higher throughput and 10-times lower latency for receipt-to-JSON processing. These are workload case studies, not general guarantees: the gains may depend on the data distribution, prior system, hardware, batching and post-training method.
If your task is narrow, a smaller model or a specialized post-training run may beat a larger general model on cost and consistency. If your task changes frequently, the multimodal generality of Nemotron or Qwen can reduce the number of separate OCR, video and language components you have to operate. Measure the cost of a completed business task, including retries and human review, rather than the raw price of one generated token.
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Governance, licensing and operational checks
“Open” does not remove the need for legal and security review. Before production, verify the license attached to the exact checkpoint, permitted commercial uses, training-data obligations, acceptable-use restrictions and the terms of any serving component. Keep sensitive documents on infrastructure you control when policy requires it, and log tool calls separately from raw document content.
Operationally, pin model and engine versions, maintain a rollback checkpoint, monitor malformed outputs and enforce least-privilege tool permissions. Test prompt-injection content inside PDFs and web pages: a vision-language agent should treat document text as untrusted data, not as an instruction that can override its system policy.
A concise selection checklist
- Do you need OCR, layout, charts, diagrams, video and tool calls in one workflow?
- Is a 120B-total model practical on your available accelerators, or does a 7B/32B/72B alternative fit better?
- Will your service run on NVIDIA hardware where the published Nemotron recipes and NVFP4 path apply?
- What is the real context length after image and video tokens are counted?
- Which errors are unacceptable: a wrong field, a missed chart trend, a bad click or an unsafe tool call?
- Can you reproduce the benchmark with your own documents, agent harness and concurrency?
If the answers favor long multimodal sessions and NVIDIA-scale serving, start with Nemotron 3.5 Super VL. If they favor smaller deployment, explicit visual-grounding features or second-level video localization, start with the appropriate Qwen2.5-VL size. Keep the other model in the test set until task-level measurements establish a clear winner.
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